Abstract: An improved system architecture uses a pipeline including a Generative Adversarial Network (GAN) including a generator neural network and a discriminator neural network to generate an image. An input image in a first domain and information about a target domain are obtained. The domains correspond to image styles. An initial latent space representation of the input image is produced by encoding the input image. An initial output image is generated by processing the initial latent space representation with the generator neural network. Using the discriminator neural network, a score is computed indicating whether the initial output image is in the target domain. A loss is computed based on the computed score. The loss is minimized to compute an updated latent space representation. The updated latent space representation is processed with the generator neural network to generate an output image in the target domain.

Abstract: Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.

Abstract: An improved system architecture uses a pipeline including an encoder and a Generative Adversarial Network (GAN) including a generator neural network to generate edited images with improved speed, realism, and identity preservation. The encoder produces an initial latent space representation of an input image by encoding the input image. The generator neural network generates an initial output image by processing the initial latent space representation of the input image. The system generates an optimized latent space representation of the input image using a loss minimization technique that minimizes a loss between the input image and the initial output image. The loss is based on target perceptual features extracted from the input image and initial perceptual features extracted from the initial output image. The system outputs the optimized latent space representation of the input image for downstream use.

Abstract: Systems and methods dynamically adjust an available range for editing an attribute in an image. An image editing system computes a metric for an attribute in an input image as a function of a latent space representation of the input image and a filtering vector for editing the input image. The image editing system compares the metric to a threshold. If the metric exceeds the threshold, then the image editing system selects a first range for editing the attribute in the input image. If the metric does not exceed the threshold, a second range is selected. The image editing system causes display of a user interface for editing the input image comprising an interface element for editing the attribute within the selected range.

Abstract: Systems and methods train an encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The encoder is trained by providing an input training image to the encoder and producing, by the encoder, a latent space representation of the input training image. The latent space representation is provided as input to the GAN to generate a generated training image. A latent code is sampled from a latent space associated with the GAN and the sampled latent code is provided as input to the GAN. The GAN generates a synthetic training image based on the sampled latent code. The sampled latent code is provided as input to the encoder to produce a synthetic training code. The encoder is updated by minimizing a loss between the generated training image and the input training image, and the synthetic training code and the sampled latent code.

Abstract: Systems and methods train and apply a specialized encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The specialized encoder neural network includes an input layer, a feature extraction layer, and a bottleneck layer positioned after the feature extraction layer. The projection process includes providing an input image to the encoder and producing, by the encoder, a latent space representation of the input image. Producing the latent space representation includes extracting a feature vector from the feature extraction layer, providing the feature vector to the bottleneck layer as input, and producing the latent space representation as output. The latent space representation produced by the encoder is provided as input to the GAN, which generates an output image based upon the latent space representation. The encoder is trained using specialized loss functions including a segmentation loss and a mean latent loss.

Abstract: One example method involves operations for receiving a request to transform an input image into a target image. Operations further include providing the input image to a machine learning model trained to adapt images. Training the machine learning model includes accessing training data having a source domain of images and a target domain of images with a target style. Training further includes using a pre-trained generative model to generate an adapted source domain of adapted images having the target style. The adapted source domain is generated by determining a rate of change for parameters of the target style, generating weighted parameters by applying a weight to each of the parameters based on their respective rate of change, and applying the weighted parameters to the source domain. Additionally, operations include using the machine learning model to generate the target image by modifying parameters of the input image using the target style.

Abstract: In implementations of adding color to digital images, an image colorization system can display a digital image to be color adjusted in an image editing interface and convert pixel content of the digital image to a LAB color space. The image colorization system can determine a lightness value (L) in the LAB color space of the pixel content of the digital image at a specified point on the digital image, and determine colors representable in an RGB color space based on combinations of A,B value pairs with the lightness value (L) in the LAB color space. The image colorization system can then determine a range of the colors for display in a color gamut in the image editing interface, the range of the colors corresponding to the A,B value pairs with the lightness value (L) of the pixel content at the specified point on the digital image.

Abstract: A viscous damped deflector in accordance with an embodiment of the present disclosure includes at least one sloped spiral rim extending from a lower surface of a top of the viscous damping chamber that rotates with the rotating distributor of the damped deflector, such that viscous fluid in the chamber is pumped to the center of the chamber as the distributor rotates.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified digital image from extracted spatial and global codes. For example, the disclosed systems can utilize a global and spatial autoencoder to extract spatial codes and global codes from digital images. The disclosed systems can further utilize the global and spatial autoencoder to generate a modified digital image by combining extracted spatial and global codes in various ways for various applications such as style swapping, style blending, and attribute editing.

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.

Abstract: The present disclosure relates to systems, computer-implemented methods, and non-transitory computer readable medium for automatically transferring makeup from a reference face image to a target face image using a neural network trained using semi-supervised learning. For example, the disclosed systems can receive, at a neural network, a target face image and a reference face image, where the target face image is selected by a user via a graphical user interface (GUI) and the reference face image has makeup. The systems transfer, by the neural network, the makeup from the reference face image to the target face image, where the neural network is trained to transfer the makeup from the reference face image to the target face image using semi-supervised learning. The systems output for display the makeup on the target face image.

Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.

Abstract: In implementations of adding color to digital images, an image colorization system can display a digital image to be color adjusted in an image editing interface and convert pixel content of the digital image to a LAB color space. The image colorization system can determine a lightness value (L) in the LAB color space of the pixel content of the digital image at a specified point on the digital image, and determine colors representable in an RGB color space based on combinations of A,B value pairs with the lightness value (L) in the LAB color space. The image colorization system can then determine a range of the colors for display in a color gamut in the image editing interface, the range of the colors corresponding to the A,B value pairs with the lightness value (L) of the pixel content at the specified point on the digital image.

Abstract: Provided herein are novel anti-CLDN 18.2 antibodies and chimeric antigen receptors (CAR), cells or compositions comprising the same, vector or plasmid encoding anti-CLDN 18.2 CAR, anti-CLDN 18.2 antibody-drug conjugates (ADCs), bispecific antibodies containing anti-CLDN 18.2 antibody, and methods for producing the same, or using the same for detecting or treating ovarian cancer or prostate cancer. Also provided herein are anti-CLDN 18.2 antibody, compositions comprising the same, nucleic acid sequence encoding the same, and a kit for detecting CLDN 18.2.

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.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing an edge prediction neural network and edge-guided colorization neural network to transform grayscale digital images into colorized digital images. In one or more embodiments, the disclosed systems apply a color edge prediction neural network to a grayscale image to generate a color edge map indicating predicted chrominance edges. The disclosed systems can present the color edge map to a user via a colorization graphical user interface and receive user color points and color edge modifications. The disclosed systems can apply a second neural network, an edge-guided colorization neural network, to the color edge map or a modified edge map, user color points, and the grayscale image to generate an edge-constrained colorized digital image.

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.

Abstract: An additive pack which comprises a water-soluble capsule containing a water-dispersible polymer powder (dispersion powder) is used to prepare aqueous construction materials such as cements and mortars on site, while offering set properties equivalent or superior to ready to mix dry mix formulations.

Abstract: A power take-off unit ring gear shaft, a method of manufacturing such a power take-off unit ring gear shaft and an apparatus comprising the power take-off unit ring gear shaft. The ring gear shaft of the invention has an annular shaft wall which surrounds a hollow inner cavity that extends throughout the entire gear shaft in an axial direction, and hollow portions are formed in the shaft wall. The invention realizes a lightweight product, helps to realize a lightweight entire vehicle, and meanwhile reduces machining areas, thus improving production efficiency of machining, reducing production cost of product, reducing labor intensity and improving productivity.