Network Learning Techniques (e.g., Back Propagation) Patents (Class 382/157)
  • Patent number: 11830173
    Abstract: A manufacturing method of learning data is used for making a neural network perform learning. The manufacturing method of learning data includes a first acquiring step configured to acquire an original image, a second acquiring step configured to acquire a first image as a training image generated by adding blur to the original image, and a third acquiring step configured to acquire a second image as a ground truth image generated by adding blur to the original image. A blur amount added to the second image is smaller than that added to the first image.
    Type: Grant
    Filed: March 5, 2021
    Date of Patent: November 28, 2023
    Assignee: CANON KABUSHIKI KAISHA
    Inventor: Takashi Oniki
  • Patent number: 11823392
    Abstract: A method, system and computer program product for segmenting generic foreground objects in images and videos. For segmenting generic foreground objects in videos, an appearance stream of an image in a video frame is processed using a first deep neural network. Furthermore, a motion stream of an optical flow image in the video frame is processed using a second deep neural network. The appearance and motion streams are then joined to combine complementary appearance and motion information to perform segmentation of generic objects in the video frame. Generic foreground objects are segmented in images by training a convolutional deep neural network to estimate a likelihood that a pixel in an image belongs to a foreground object. After receiving the image, the likelihood that the pixel in the image is part of the foreground object as opposed to background is then determined using the trained convolutional deep neural network.
    Type: Grant
    Filed: August 2, 2022
    Date of Patent: November 21, 2023
    Assignee: Board of Regents, The University of Texas System
    Inventors: Kristen Grauman, Suyog Dutt Jain, Bo Xiong
  • Patent number: 11823368
    Abstract: A system and methods for assessing road surface quality includes a wireless mobile device having a camera, a location receiver, and a road surface classifying computer application and is configured to be mounted on a vehicle. The system has a remote server having a road surface classifying web application, a database, and an interactive map connected to the web application. The mobile device actuates the camera to record videos, extract images from the videos, process the images, classify the images into road conditions, record a location of the images, generate a data packet including an identification of the mobile device and a time stamp of the data packet, and transmit the data packet to the remote server. The remote server stores the data packet in the database. The web application superimposes the time stamp of the data packet, the location, the road conditions, and the images on the interactive map.
    Type: Grant
    Filed: March 31, 2023
    Date of Patent: November 21, 2023
    Assignee: Prince Mohammad Bin Fahd University
    Inventors: Nazeeruddin Mohammad, Majid Ali Khan, Ahmed Abul Hasanaath
  • Patent number: 11804057
    Abstract: The techniques described herein relate to a systems and methods for a digital asset generation platform. The digital asset generation platform may ingest an ingest input. The digital asset generation platform may utilize a document identification engine corresponding to a first stage of a multi-stage convolutional neural network for identifying document types of documents. The digital asset generation platform may utilize an object detector engine corresponding to a second stage of the multi-stage convolutional neural network for detecting a dynamic mapping in the digital file. The digital asset generation platform may utilize a post-processing engine for classifying the dynamic mapping in the at least one digital file. The digital asset generation platform may dynamically generate a digital asset representative of the document based on the key value data pairs extracted from the dynamic mapping.
    Type: Grant
    Filed: March 23, 2023
    Date of Patent: October 31, 2023
    Assignee: LiquidX, Inc.
    Inventors: James Toffey, Frank Dimarco, Coby Dodd, Shayan Hemmatiyan, Venkat Naidu, Edmond Costantini, Mark Alexander, Vishal Panchamia
  • Patent number: 11804028
    Abstract: A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video.
    Type: Grant
    Filed: June 6, 2022
    Date of Patent: October 31, 2023
    Assignee: Vizit Labs, Inc.
    Inventors: Elham Saraee, Zachary Halloran, Jehan Hamedi
  • Patent number: 11804037
    Abstract: The present application provides a method and a system for generating an image sample having a specific feature. The method includes: training a generative adversarial network-based sample generation model, where the generative adversarial network includes a generator and two discriminators: a global discriminator configured to perform global discrimination on an image, and a local discriminator configured to perform local discrimination on a specific feature; and inputting, to a trained generator that serves as a sample generation model, a semantic segmentation image that indicates a location of the specific feature and a corresponding real image not having the specific feature, to obtain a generated image sample having the specific feature.
    Type: Grant
    Filed: June 9, 2023
    Date of Patent: October 31, 2023
    Assignee: CONTEMPORARY AMPEREX TECHNOLOGY CO., LIMITED
    Inventors: Guannan Jiang, Jv Huang, Chao Yuan
  • Patent number: 11790066
    Abstract: Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.
    Type: Grant
    Filed: September 13, 2021
    Date of Patent: October 17, 2023
    Assignee: Private Identity LLC
    Inventor: Scott Edward Streit
  • Patent number: 11783190
    Abstract: A method for ascertaining an explanation map of an image. All those pixels of the image are highlighted which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is being selected in such a way that it selects a smallest possible subset of the pixels of the image as relevant. The explanation map leads to the same classification result as the image when the explanation map is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.
    Type: Grant
    Filed: July 3, 2019
    Date of Patent: October 10, 2023
    Assignee: ROBERT BOSCH GMBH
    Inventors: Joerg Wagner, Tobias Gindele, Jan Mathias Koehler, Jakob Thaddaeus Wiedemer, Leon Hetzel
  • Patent number: 11775836
    Abstract: A neural network in multi-task deep learning paradigm for machine vision includes an encoder that further includes a first, a second, and a third tier. The first tier comprises a first-tier unit having one or more first-unit blocks. The second tier receives a first-tier output from the first tier at one or more second-tier units in the second tier, a second-tier unit comprises one or more second-tier blocks, the third tier receives a second-tier output from the second tier at one or more third-tier units in the third tier, and a third-tier block comprises one or more third-tier blocks. The neural network further comprises a decoder operatively the encoder to receive an encoder output from the encoder as well as one or more loss function layers that are configured to backpropagate one or more losses for training at least the encoder of the neural network in a deep learning paradigm.
    Type: Grant
    Filed: May 20, 2020
    Date of Patent: October 3, 2023
    Assignee: Magic Leap, Inc.
    Inventors: Prajwal Chidananda, Ayan Tuhinendu Sinha, Adithya Shricharan Srinivasa Rao, Douglas Bertram Lee, Andrew Rabinovich
  • Patent number: 11769328
    Abstract: Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.
    Type: Grant
    Filed: September 15, 2021
    Date of Patent: September 26, 2023
    Assignee: Gracenote, Inc.
    Inventors: Konstantinos Antonio Dimitriou, Amanmeet Garg
  • Patent number: 11763544
    Abstract: In an approach to augmenting a caption dataset by leveraging a denoising autoencoder to sample and generate additional captions from the ground truth captions, one or more computer processors generate a plurality of new captions utilizing an autoencoder fed with one or more noisy captions, wherein the autoencoder is trained with a dataset comprising a plurality of ground truth captions. The one or more computer processors calculate an importance weight for each new caption in the plurality of generated new captions as compared to a plurality of associated ground truth captions based on a consensus metric. The one or more computer processors train a caption model with the generated plurality of new captions and associated calculated weights.
    Type: Grant
    Filed: July 7, 2020
    Date of Patent: September 19, 2023
    Assignee: International Business Machines Corporation
    Inventors: Shiwan Zhao, Hao Kai Zhang, Yi Ke Wu, Zhong Su
  • Patent number: 11762622
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for remotely generating modified digital images utilizing an interactive image editing architecture. For example, the disclosed systems receive an image editing request for remotely editing a digital image utilizing an interactive image editing architecture. In some cases, the disclosed systems maintain, via a canvas worker container, a digital stream that reflects versions of the digital image. The disclosed systems determine, from the digital stream utilizing the canvas worker container, an image differential metric indicating a difference between a first version of the digital image and a second version of the digital image associated with the image editing request. Further, the disclosed systems provide the image differential metric to a client device for rendering the second version of the digital image to reflect a modification corresponding to the user interaction.
    Type: Grant
    Filed: May 16, 2022
    Date of Patent: September 19, 2023
    Assignee: Adobe Inc.
    Inventors: Sven Olsen, Shabnam Ghadar, Baldo Faieta, Akhilesh Kumar
  • Patent number: 11755913
    Abstract: A method in which a convolutional neural network is configured to receive an input data structure including a group of values corresponding to signal samples and to generate a corresponding classification output indicative of a selected one among plural predefined classes. The convolutional neural network includes an ordered sequence of layers, each configured to receive a corresponding layer input data structure including a group of input values, and generate a corresponding layer output data structure including a group of output values by convolving the layer input data structure with at least one corresponding filter including a corresponding group of weights. The layer input data structure of the first layer of the sequence corresponds to the input data structure. The layer input data structure of a generic layer of the sequence different from the first layer corresponds to the layer output data structure generated by a previous layer in the sequence.
    Type: Grant
    Filed: March 11, 2016
    Date of Patent: September 12, 2023
    Assignee: TELECOM ITALIA S.p.A
    Inventors: Gianluca Francini, Skjalg Lepsoy, Pedro Porto Buarque De Gusmao
  • Patent number: 11748943
    Abstract: An electronic device and method of dataset cleaning is provided. The electronic device receives a dataset comprising a plurality of samples, of which a first sample comprises a 2D image of an object of interest and a 3D shape model of the object of interest. The electronic device determines 2D landmarks from the 2D image and extracts 3D landmarks from the 3D shape model. The electronic device computes an error between the determined 2D landmarks and corresponding 2D locations of the extracted 3D landmarks on the 2D image, based on an error metric. Thereafter, the electronic device determines the computed error to be above a threshold. Based on the determination that the computed error is above the threshold, the electronic device updates the dataset by a removal of the first sample from the dataset and trains a neural network on a task of 3D reconstruction, based on the updated dataset.
    Type: Grant
    Filed: February 8, 2021
    Date of Patent: September 5, 2023
    Assignee: SONY GROUP CORPORATION
    Inventors: Jong Hwa Lee, Seunghan Kim, Gary Lyons
  • Patent number: 11741581
    Abstract: Embodiments of this application disclose a training method using image processing model for processing blurry images. The method includes obtaining a sample pair comprising a clear image and a corresponding blurry image; the sharpness of the clear image being greater than a preset threshold, the sharpness of the blurry image being less than the preset threshold; activating the image processing model to perform sharpness restoration on the blurry image to obtain a restored image; and updating network parameters of a first network and network parameters of a second network in the image processing model according to the restored image and the clear image to obtain a trained image processing model; the network parameters of the first network and the network parameters of the second network meeting a selective sharing condition indicating whether the network parameters between the first network and the second network are shared or independent.
    Type: Grant
    Filed: May 27, 2021
    Date of Patent: August 29, 2023
    Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
    Inventors: Hongyun Gao, Xin Tao, Jiaya Jia, Yuwing Tai, Xiaoyong Shen
  • Patent number: 11734572
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.
    Type: Grant
    Filed: August 17, 2020
    Date of Patent: August 22, 2023
    Assignee: DeepMind Technologies Limited
    Inventors: Maxwell Elliot Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu
  • Patent number: 11734577
    Abstract: A method for an electronic apparatus to perform an operation of an artificial intelligence model includes acquiring resource information for hardware of the electronic apparatus while a plurality of data used for an operation of a neural network model are stored in a memory, the plurality of data respectively having degrees of importance different from each other; obtaining data to be used for the operation of the neural network model among the plurality of data according to the degrees of importance of each of the plurality of data based on the acquired resource information; and performing the operation of the neural network model by using the obtained data.
    Type: Grant
    Filed: May 18, 2020
    Date of Patent: August 22, 2023
    Assignee: SAMSUNG ELECTRONICS CO., LTD
    Inventors: Sejung Kwon, Dongsoo Lee
  • Patent number: 11734955
    Abstract: A system and method for identifying a subject using imaging are provided. In some aspects, the method includes receiving an image depicting a subject to be identified, and applying a trained Disentangled Representation learning-Generative Adversarial Network (DR-GAN) to the image to generate an identity representation of the subject, wherein the DR-GAN comprises a discriminator and a generator having at least one of an encoder and a decoder. The method also includes identifying the subject using the identity representation, and generating a report indicative of the subject identified.
    Type: Grant
    Filed: September 18, 2018
    Date of Patent: August 22, 2023
    Assignee: BOARD OF TRUSTEES OF MICHIGAN STATE UNIVERSITY
    Inventors: Xiaoming Liu, Luan Quoc Tran, Xi Yin
  • Patent number: 11727278
    Abstract: A computer-implemented method, a computing system, and a computer program product for generating new items compatible with given items may use data associated with a plurality of images and random noise data associated with a random noise image to train an adversarial network including a series of generator networks and a series of discriminator networks corresponding to the series of generator networks by modifying, using a loss function of the adversarial network that depends on a compatibility of the images, one or more parameters of the series of generator networks. The series of generator networks may generate a generated image associated with a generated item different than the given items.
    Type: Grant
    Filed: June 15, 2021
    Date of Patent: August 15, 2023
    Assignee: Visa International Service Association
    Inventors: Ablaikhan Akhazhanov, Maryam Moosaei, Hao Yang
  • Patent number: 11727273
    Abstract: The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.
    Type: Grant
    Filed: December 3, 2021
    Date of Patent: August 15, 2023
    Inventors: Alexandru Malaescu, Adrian Dorin Capata, Mihai Ciuc, Alina Sultana, Dan Filip, Liviu-Cristian Dutu
  • Patent number: 11712162
    Abstract: A system for testing and/or training the vision of a user is disclosed herein. The system includes at least one camera, a visual display device having an output screen, and a data processing device operatively coupled to the at least one camera and the visual display device. In one embodiment, the data processing device is programmed to determine a head position, head velocity, and/or head speed of a user during a vision test or vision training routine from a plurality of images of the head of the user captured by the at least one camera. In another embodiment, the data processing device is programmed to determine, based upon an input signal received from a user input device, a contrast display setting for a screen background relative to at least one visual target, the contrast display setting enabling the user to gradually adapt to increasing levels of visual stimulation.
    Type: Grant
    Filed: May 23, 2022
    Date of Patent: August 1, 2023
    Assignee: Bertec Corporation
    Inventors: Necip Berme, Mohan Chandra Baro, Cameron Scott Hobson
  • Patent number: 11710567
    Abstract: Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.
    Type: Grant
    Filed: October 22, 2019
    Date of Patent: July 25, 2023
    Assignee: FUJIFILM Corporation
    Inventor: Kenta Yamada
  • Patent number: 11694078
    Abstract: An electronic apparatus may include a memory that stores first information regarding a plurality of first artificial intelligence models trained to perform image processing differently from each other and second information regarding a second artificial intelligence model trained to identify a type of an image by predicting a processing result of the image by each of the plurality of first artificial intelligence models. The electronic apparatus may further include a processor configured to identify a type of an input image by inputting the input image to the second artificial intelligence model stored in the memory, and process the input image by inputting the input image to one of the plurality of first intelligence models stored in the memory based on the identified type.
    Type: Grant
    Filed: October 27, 2020
    Date of Patent: July 4, 2023
    Assignee: SAMSUNG ELECTRONICS CO., LTD.
    Inventors: Yongmin Tai, Insang Cho, Chanyoung Hwang
  • Patent number: 11694085
    Abstract: A method of training a generator G of a Generative Adversarial Network (GAN) includes receiving, by an encoder E, a target data Y; receiving, by the encoder E, an output G(Z) of the generator G, where the generator G generates the output G(Z) in response to receiving a random sample Z and where a discriminator D of the GAN is trained to distinguish which of the G(Z) and the target data Y; training the encoder E to minimize a difference between a first latent space representation E(G(Z)) of the output G(Z) and a second latent space representation E(Y) of the target data Y, where the output G(Z) and the target data Y are input to the encoder E; and using the first latent space representation E(G(Z)) and the second latent space representation E(Y) to constrain the training of the generator G.
    Type: Grant
    Filed: May 19, 2021
    Date of Patent: July 4, 2023
    Assignee: Agora Lab, Inc.
    Inventor: Sheng Zhong
  • Patent number: 11681918
    Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.
    Type: Grant
    Filed: April 21, 2021
    Date of Patent: June 20, 2023
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Patent number: 11676700
    Abstract: A system for recording, storing and processing diagnostic information, including: a computer implementing a computer-readable media including digital data and ground truth; a registry constructed and arranged to store and associate transactions or accesses on the data; and a machine learning system that considers each learning step modification a microtransaction for the data used in that step and which is recorded in the transaction registry. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
    Type: Grant
    Filed: April 22, 2021
    Date of Patent: June 13, 2023
    Assignee: Digital Diagnostics Inc.
    Inventor: Michael D. Abramoff
  • Patent number: 11676362
    Abstract: According to one embodiment, a training system includes a first generator, a second generator, a third generator, and a trainer. The first generator uses a human body model to generate a first image. The human body model models a human body and is three-dimensional and virtual. The second generator generates a teacher image by annotating body parts of the human body model in the first image. The third generator generates a second image including noise by performing, on the first image, at least one selected from first processing, second processing, third processing, fourth processing, or fifth processing. The trainer uses the second image and the teacher image to train a first model.
    Type: Grant
    Filed: September 9, 2020
    Date of Patent: June 13, 2023
    Assignee: KABUSHIKI KAISHA TOSHIBA
    Inventor: Yasuo Namioka
  • Patent number: 11676408
    Abstract: A computer that identifies a fake image is described. During operation, the computer receives an image. Then, the computer performs analysis on the image to determine a signature that includes multiple features. Based at least in part in the determined signature, the computer classifies the image as having a first signature associated with the fake image or as having a second signature associated with a real image, where the first signature corresponds to a finite resolution of a neural network that generated the fake image, a finite number of parameters in the neural network that generated the fake image, or both. For example, the finite resolution may correspond to floating point operations in the neural network. Moreover, in response to the classification, the computer may perform a remedial action, such as providing a warning or a recommendation, or performing filtering.
    Type: Grant
    Filed: February 23, 2021
    Date of Patent: June 13, 2023
    Assignee: Artificial Intelligence Foundation, Inc.
    Inventors: Matthias Nießner, Gaurav Bharaj
  • Patent number: 11669730
    Abstract: A recognition apparatus and a training method are provided. The recognition apparatus includes a memory configured to store a neural network including a previous layer of neurons, and a current layer of neurons that are activated based on first synaptic signals and second synaptic signals, the first synaptic signals being input from the previous layer, and the second synaptic signals being input from the current layer. The recognition apparatus further includes a processor configured to generate a recognition result based on the neural network. An activation neuron among the neurons of the current layer generates a first synaptic signal to excite or inhibit neurons of a next layer, and generates a second synaptic signal to inhibit neurons other than the activation neuron in the current layer.
    Type: Grant
    Filed: October 30, 2019
    Date of Patent: June 6, 2023
    Assignee: SAMSUNG ELECTRONICS CO., LTD.
    Inventor: Jun Haeng Lee
  • Patent number: 11670071
    Abstract: In accordance with implementations of the subject matter described herein, a solution for fine-grained image recognition is proposed. This solution includes extracting a global feature of an image using a first sub-network of a first learning network; determining a first attention region of the image based on the global feature using a second sub-network of the first learning network, the first attention region including a discriminative portion of an object in the image; extracting a first local feature of the first attention region using a first sub-network of a second learning network; and determining a category of the object in the image based at least in part on the first local feature. Through this solution, it is possible to localize an image region at a finer scale accurately such that a local feature at a fine scale can be obtained for object recognition.
    Type: Grant
    Filed: May 29, 2018
    Date of Patent: June 6, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jianlong Fu, Tao Mei
  • Patent number: 11669940
    Abstract: An apparatus for baseline estimation in input signal data is configured to retrieve input signal data (I(xi)) and to subtract baseline estimation data (ƒ(xi)) from the input signal data (I(xi)) to compute output signal data. The apparatus is further configured to compute the baseline estimation data (ƒ(xi)) from a convolution using a discrete Green's function (G(xi)).
    Type: Grant
    Filed: March 29, 2019
    Date of Patent: June 6, 2023
    Assignee: LEICA MICROSYSTEMS CMS GMBH
    Inventors: Kai Walter, Florian Ziesche
  • Patent number: 11645833
    Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.
    Type: Grant
    Filed: November 17, 2021
    Date of Patent: May 9, 2023
    Inventors: Ali Madani, Mehdi Moradi, Tanveer F. Syeda-Mahmood
  • Patent number: 11625846
    Abstract: Systems and methods described herein relate to training a machine-learning-based monocular depth estimator. One embodiment selects a virtual image in a virtual dataset, the virtual dataset including a plurality of computer-generated virtual images; generates, from the virtual image in accordance with virtual-camera intrinsics, a point cloud in three-dimensional space based on ground-truth depth information associated with the virtual image; reprojects the point cloud back to two-dimensional image space in accordance with real-world camera intrinsics to generate a transformed virtual image; and trains the machine-learning-based monocular depth estimator, at least in part, using the transformed virtual image.
    Type: Grant
    Filed: March 25, 2021
    Date of Patent: April 11, 2023
    Assignee: Toyota Research institute, Inc.
    Inventors: Vitor Guizilini, Rares A. Ambrus, Adrien David Gaidon, Jie Li
  • Patent number: 11610420
    Abstract: Systems and methods for human detection are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes humans in one or more different scenes. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
    Type: Grant
    Filed: December 21, 2020
    Date of Patent: March 21, 2023
    Inventors: Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Manmohan Chandraker, Jong-Chyi Su
  • Patent number: 11610284
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a synthesized signal. In some implementations, a computer-implemented system obtains generator input data including at least an input signal having one or more first characteristics, processes the generator input data to generate output data including a synthesized signal having one or more second characteristics using a generator neural network, and outputs the synthesized signal to a device. The generator neural network is trained, based on a plurality of training examples, with a discriminator neural network.
    Type: Grant
    Filed: July 9, 2021
    Date of Patent: March 21, 2023
    Assignee: X Development LLC
    Inventor: Eliot Julien Cowan
  • Patent number: 11603749
    Abstract: A method can include receiving multi-channel time series data of drilling operations; training a deep neural network (DNN) using the multi-channel time series data to generate a trained deep neural network as part of a computational simulator where the deep neural network includes at least one recurrent unit; simulating a drilling operation using the computational simulator to generate a simulation result; and rendering the simulation result to a display.
    Type: Grant
    Filed: November 15, 2018
    Date of Patent: March 14, 2023
    Assignee: Schlumberger Technology Corporation
    Inventors: Yingwei Yu, Sylvain Chambon, Qiuhua Liu
  • Patent number: 11604945
    Abstract: Systems and methods for lane marking and road sign recognition are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes one or more road scenes having lane markings and road signs. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
    Type: Grant
    Filed: December 21, 2020
    Date of Patent: March 14, 2023
    Inventors: Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Manmohan Chandraker, Jong-Chyi Su
  • Patent number: 11594041
    Abstract: Systems and methods for obstacle detection are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes one or more road scenes having obstacles. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
    Type: Grant
    Filed: December 21, 2020
    Date of Patent: February 28, 2023
    Inventors: Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Manmohan Chandraker, Jong-Chyi Su
  • Patent number: 11593616
    Abstract: The present invention relates to a method for determining a data item's membership in a database, the method comprising: a supervised training phase to obtain three trained neural networks, a phase of preparing the database by application of the first trained network to each data item of the base, and a utilization phase comprising the step of: using the first network on the data item, obtaining a binary value representative of the identity between the data item and a data item of the base by application of the third network, and selecting of those data items of the database for which the binary value obtained corresponds to an identity between the data item and the data items.
    Type: Grant
    Filed: December 13, 2017
    Date of Patent: February 28, 2023
    Assignee: THALES
    Inventors: Pierre Bertrand, Benoît Huyot, Sandra Cremer
  • Patent number: 11586919
    Abstract: A task-based learning using task-directed prediction network can be provided. Training data can be received. Contextual information associated with a task-based criterion can be received. A machine learning model can be trained using the training data. A loss function computed during training of the machine learning model integrates the task-based criterion, and minimizing the loss function during training iterations includes minimizing the task-based criterion.
    Type: Grant
    Filed: June 12, 2020
    Date of Patent: February 21, 2023
    Assignee: International Business Machines Corporation
    Inventors: Yada Zhu, Di Chen, Xiaodong Cui, Upendra Chitnis, Kumar Bhaskaran, Wei Zhang
  • Patent number: 11580334
    Abstract: Systems and methods for construction zone segmentation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes construction zones scenes having various objects. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
    Type: Grant
    Filed: December 21, 2020
    Date of Patent: February 14, 2023
    Inventors: Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Manmohan Chandraker, Jong-Chyi Su
  • Patent number: 11574185
    Abstract: A method for training a deep neural network according to an embodiment includes training a deep neural network model using a first data set including a plurality of labeled data and a second data set including a plurality of unlabeled data, assigning a ground-truth label value to some of the plurality of unlabeled data, updating the first data set and the second data set such that the data to which the ground-truth label value is assigned is included in the first data set, and further training the deep neural network model using the updated first data set and the updated second data set.
    Type: Grant
    Filed: October 28, 2019
    Date of Patent: February 7, 2023
    Assignee: SAMSUNG SDS CO., LTD.
    Inventors: Jong-Won Choi, Young-Joon Choi, Ji-Hoon Kim, Byoung-Jip Kim, Seong-Won Bak
  • Patent number: 11568245
    Abstract: The present invention provides artificial intelligence technology which has machine-learning-based information understanding capability, including metric learning providing improved classification performance, classification of an object considering a semantic relationship, understanding of the meaning of a scene based on the metric learning and the classification, and the like. An electronic device according to one embodiment of the present invention comprises a memory in which at least one instruction is stored, and a processor for executing the stored instruction. Here, the processor extracts feature data from training data of a first class, obtains a feature point by mapping the extracted feature data to an embedding space, and makes an artificial neural network learn in a direction for reducing a distance between the obtained feature point and an anchor point.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: January 31, 2023
    Assignee: SAMSUNG ELECTRONICS CO., LTD.
    Inventors: Tae Kwon Chang, In Kwon Choi, Jae Hyun Park
  • Patent number: 11568576
    Abstract: Techniques are generally described for generation of photorealistic synthetic image data. A generator network generates first synthetic image data. A first class of image data represented by a first portion of the first synthetic image data is detected and the first portion is sent to a first discriminator network. The first discriminator network generates a prediction of whether the first portion of the first synthetic image data is synthetically generated. A second class of image data represented by a second portion of the first synthetic image data is detected and the second portion is sent to a second discriminator network. The second discriminator network generates a prediction of whether the second portion of the first synthetic image data is synthetically generated. The generator network is updated based on the predictions of the discriminators.
    Type: Grant
    Filed: December 10, 2020
    Date of Patent: January 31, 2023
    Assignee: AMAZON TECHNOLOGIES, INC.
    Inventors: Aleix Margarit Martinez, Raghu Deep Gadde, Qianli Feng, Alexandru Indrei, Gerard Gjonej
  • Patent number: 11568178
    Abstract: Various techniques are provided for training a neural network to classify images. A convolutional neural network (CNN) is trained using training dataset comprising a plurality of synthetic images. The CNN training process tracks image-related metrics and other informative metrics as the training dataset is processed. The trained inference CNN may then be tested using a validation dataset of real images to generate performance results (e.g., whether a training image was properly or improperly labeled by the trained inference CNN). In one or more embodiments, a training dataset and analysis engine extracts and analyzes the informative metrics and performance results, generates parameters for a modified training dataset to improve CNN performance, and generates corresponding instructions to a synthetic image generator to generate a new training dataset. The process repeats in an iterative fashion to build a final training dataset for use in training an inference CNN.
    Type: Grant
    Filed: January 11, 2021
    Date of Patent: January 31, 2023
    Assignee: Teledyne FLIR Commercial Systems, Inc.
    Inventor: Pierre Boulanger
  • Patent number: 11562166
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating shift-resilient neural network outputs based on utilizing a dense pooling layer, a low-pass filter layer, and a downsampling layer of a neural network. For example, the disclosed systems can generate a pooled feature map utilizing a dense pooling layer to densely pool feature values extracted from an input. The disclosed systems can further apply a low-pass filter to the pooled feature map to generate a shift-adaptive feature map. In addition, the disclosed systems can downsample the shift-adaptive feature map utilizing a downsampling layer. Based on the downsampled, shift-adaptive feature map, the disclosed systems can generate shift-resilient neural network outputs such as digital image classifications.
    Type: Grant
    Filed: May 21, 2021
    Date of Patent: January 24, 2023
    Assignee: Adobe Inc.
    Inventor: Richard Zhang
  • Patent number: 11544498
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.
    Type: Grant
    Filed: March 5, 2021
    Date of Patent: January 3, 2023
    Assignee: Google LLC
    Inventors: Ariel Gordon, Soeren Pirk, Anelia Angelova, Vincent Michael Casser, Yao Lu, Anthony Brohan, Zhao Chen, Jan Dlabal
  • Patent number: 11537813
    Abstract: During a training phase, a first machine learning system is trained using actual data, such as multimodal images of a hand, to generate synthetic image data. During training, the first system determines latent vector spaces associated with identity, appearance, and so forth. During a generation phase, latent vectors from the latent vector spaces are generated and used as input to the first machine learning system to generate candidate synthetic image data. The candidate image data is assessed to determine suitability for inclusion into a set of synthetic image data that may be used for subsequent use in training a second machine learning system to recognize an identity of a hand presented by a user. For example, the candidate synthetic image data is compared to previously generated synthetic image data to avoid duplicative synthetic identities. The second machine learning system is then trained using the approved candidate synthetic image data.
    Type: Grant
    Filed: September 30, 2020
    Date of Patent: December 27, 2022
    Assignee: AMAZON TECHNOLOGIES, INC.
    Inventors: Igor Kviatkovsky, Nadav Israel Bhonker, Alon Shoshan, Manoj Aggarwal, Gerard Guy Medioni
  • Patent number: 11514574
    Abstract: Various methods for the detection and enhanced visualization of a particular structure or pathology of interest in a human eye are discussed in the present disclosure. An example method to visualize a given pathology (e.g., CNV) in an eye includes collecting optical coherence tomography (OCT) image data of the eye from an OCT system. The OCT image data is segmented to identify two or more retinal layer boundaries located in the eye. The two or more retinal layer boundaries are located at different depth locations in the eye. One of the identified layer boundaries is moved and reshaped to optimize visualization of the pathology located between the identified layer boundaries. The optimized visualization is displayed or stored or for a further analysis thereof.
    Type: Grant
    Filed: September 23, 2020
    Date of Patent: November 29, 2022
    Assignee: CARL ZEISS MEDITEC, INC.
    Inventors: Homayoun Bagherinia, Luis De Sisternes
  • Patent number: 11507774
    Abstract: A method for selecting a deep learning network which is optimal for solving an image processing task obtaining a type of the image processing task, selecting a data set according to the type of problem, and dividing selected data set into training data and test data. Similarities between different training data are calculated, and a batch size of the training data is adjusted according to the similarities of the training data. A plurality of deep learning networks is selected according to the type of problem, and the plurality of deep learning networks is trained through the training data to obtain network models. Each of the network models is tested through the test data, and the optimal deep learning network with the best test result is selected from the plurality of deep learning networks appropriate for image processing.
    Type: Grant
    Filed: April 9, 2021
    Date of Patent: November 22, 2022
    Assignee: HON HAI PRECISION INDUSTRY CO., LTD.
    Inventors: Tung-Tso Tsai, Chin-Pin Kuo, Guo-Chin Sun, Tzu-Chen Lin, Wan-Jhen Lee