Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to input a fisheye image to a vector quantized variational autoencoder. The vector quantized variational autoencoder can encode the fisheye image to first latent variables based on an encoder. The vector quantized variational autoencoder can quantize the first latent variables to generate second latent variables based on a dictionary of embeddings. The vector quantized variational autoencoder can decode the second latent variables to a rectified rectilinear image using a decoder and output the rectified rectilinear image.

Abstract: A system comprises a computer including a processor and a memory. The memory storing instructions executable by the processor to cause the processor to generate a low-level representation of the input source domain data; generate an embedding of the input source domain data; generate a high-level feature representation of features of the input source domain data; generate output target domain data in the target domain that includes semantics corresponding to the input source domain data by processing the high-level feature representation of the features of the input source domain data using a domain low-level decoder neural network layer that generate data from the target; and modify a loss function such that latent attributes corresponding to the embedding are selected from a same probability distribution.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a second convolutional neural network (CNN) training dataset by determining an underrepresented object configuration and an underrepresented noise factor corresponding to an object in a first CNN training dataset, generate one or more simulated images including the object corresponding to the underrepresented object configuration in the first CNN training dataset by inputting ground truth data corresponding to the object into a photorealistic rendering engine and generate one or more synthetic images including the object corresponding to the underrepresented noise factor in the first CNN training dataset by processing the simulated images with a generative adversarial network (GAN) to determine a second CNN training dataset.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor train a deep neural network based on plurality of real-world images, determine the accuracy of the deep neural network is below a threshold based on identifying one or more physical features by the deep neural network, including one or more object types, in the plurality of real-world images and generate a plurality of synthetic images based on the accuracy of the deep neural network is below a threshold based on identifying the one or more physical features using a photo-realistic image rendering software program and a generative adversarial network. The instructions can include further instructions to retrain the deep neural network based on the plurality of real-world images and the plurality of synthetic images and output the retrained deep neural network.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to train a deep neural network based on a plurality of real-world images, determine the accuracy of the deep neural network is below a threshold based on identifying one or more physical features by the deep neural network, including one or more object types, in the plurality of real-world images and generate a plurality of synthetic images based on the accuracy of the deep neural network is below a threshold based on identifying the one or more physical features using a photo-realistic image rendering software program and a generative adversarial network. The instructions can include further instructions to retrain the deep neural network based on the plurality of real-world images and the plurality of synthetic images and output the retrained deep neural network.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input an image to a first layer of a machine learning program, the first layer trained to identify one or more quadrilateral regions in the image, upon identifying the one or more quadrilateral regions, input the collected image to a second layer of a machine learning program, the second layer trained to identify a plurality of sets of vertices, each set of vertices defining a respective polygonal area, identify one of the polygonal areas in which to park a vehicle, and actuate one or more vehicle components to move the vehicle into the identified polygonal area.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate two or more stereo pairs of synthetic images and generate two or more stereo pairs of real images based on the two or more stereo pairs of synthetic images using a generative adversarial network (GAN), wherein the GAN is trained using a six-axis degree of freedom (DoF) pose determined based on the two or more pairs of real images. The instructions can further include instructions to train a deep neural network based on a sequence of real images and operate a vehicle using the deep neural network to process a sequence of video images acquired by a vehicle sensor.

Abstract: The present disclosure discloses a system and a method that includes receiving, at a decoder, a latent representation of an image having a first domain, and generating a reconstructed image having a second domain, wherein the reconstructed image is generated based on the latent representation.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input an image to a first layer of a machine learning program, the first layer trained to identify one or more quadrilateral regions in the image, upon identifying the one or more quadrilateral regions, input the collected image to a second layer of a machine learning program, the second layer trained to identify a plurality of sets of vertices, each set of vertices defining a respective polygonal area, identify one of the polygonal areas in which to park a vehicle, and actuate one or more vehicle components to move the vehicle into the identified polygonal area.

Abstract: The present disclosure discloses a system and a method. In an example implementation, the system and the method generate, at a first encoder neural network, an encoded representation of image features of an image received from a vehicle sensor of a vehicle. The system and method can also generate, at a second encoder neural network, an encoded representation of a map tile features and generate, at the decoder neural network, a semantically segmented map tile based on the encoded representation of image features, the encoded representation of map tile features, and Global Positioning System (GPS) coordinates of the vehicle. The semantically segmented map tile includes a location of the vehicle and detected objects depicted within the image with respect to the vehicle.

Abstract: The present disclosure discloses a system and a method. In an example implantation, the system and the method can generate, at a discriminator, a plurality of image patches from an image, determine a plurality of routing coefficients within a capsule network based on the plurality of image patches, generate a prediction indicating whether the image is synthetic or sourced from a real distribution based on the plurality of routing coefficients, and update one or more weights of a generator based on the prediction, wherein the generator is connected to the discriminator.

Abstract: The present disclosure discloses a system and a method. The system and the method generate, via a deep neural network, a first synthetic image based on a simulated image, generate a segmentation mask based on the synthetic image, compare the segmentation mask with a ground truth mask of the synthetic image, update the deep neural network based on the comparison, and generate, via the updated deep neural network, a second synthetic image based on the simulated image.

Abstract: A training system for a neural network system and method of training is disclosed. The method may comprise: receiving, from a sensor, an image frame captured while an operator is controlling a vehicle; using an eye-tracking system associated with the sensor, monitoring the eyes of the operator to determine eyeball gaze data; determining, from the image frame, a plurality of pedestrians; and iteratively training the neural network system to determine, from among the plurality of pedestrians, the one or more target pedestrians using the eyeball gaze data and an answer dataset that is based on the eyeball gaze data, wherein the determined one or more target pedestrians have a relatively-higher probability of collision with the vehicle than a remainder of the plurality of pedestrians.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a synthetic image and corresponding ground truth and generate a plurality of domain adapted synthetic images by processing the synthetic image with a variational auto encoder-generative adversarial network (VAE-GAN), wherein the VAE-GAN is trained to adapt the synthetic image from a first domain to a second domain. The instructions can include further instructions to train a deep neural network (DNN) based on the domain adapted synthetic images and the corresponding ground truth and process images with the trained deep neural network to determine objects.

Abstract: The present disclosure discloses a system and a method. The system and the method generate, via a deep neural network, a first synthetic image based on a simulated image, generate a segmentation mask based on the synthetic image, compare the segmentation mask with a ground truth mask of the synthetic image, update the deep neural network based on the comparison, and generate, via the updated deep neural network, a second synthetic image based on the simulated image.

Abstract: The present disclosure discloses a system and a method. In an example implantation, the system and the method can receive a synthetic image at a first deep neural network; and determine, via the first deep neural network, a prediction indicative of whether the synthetic image is machine-generated or is sourced from the real data distribution. The prediction can comprise a quantitative measure of photorealism of synthetic image.

Abstract: The present disclosure discloses a system and a method. In an example implementation, the system and the method can receive a synthetic image at a first deep neural network, and determine, via the first deep neural network, a prediction indicative of whether the synthetic image is machine-generated or is sourced from the real data distribution. The prediction can comprise a quantitative measure of photorealism of synthetic image.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate two or more stereo pairs of synthetic images and generate two or more stereo pairs of real images based on the two or more stereo pairs of synthetic images using a generative adversarial network (GAN), wherein the GAN is trained using a six-axis degree of freedom (DoF) pose determined based on the two or more pairs of real images. The instructions can further include instructions to train a deep neural network based on a sequence of real images and operate a vehicle using the deep neural network to process a sequence of video images acquired by a vehicle sensor.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a pair of synthetic stereo images and a corresponding synthetic depth map with an image synthesis engine wherein the synthetic stereo images correspond to real stereo images acquired by a stereo camera and the synthetic depth map is a three-dimensional (3D) map corresponding to a 3D scene viewed by the stereo camera and process each image of the pair of synthetic stereo images pair independently using a generative adversarial network (GAN) to generate a fake image, wherein the fake image corresponds to one of the synthetic stereo images.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a synthetic image and corresponding ground truth and generate a plurality of domain adapted synthetic images by processing the synthetic image with a variational auto encoder-generative adversarial network (VAE-GAN), wherein the VAE-GAN is trained to adapt the synthetic image from a first domain to a second domain. The instructions can include further instructions to train a deep neural network (DNN) based on the domain adapted synthetic images and the corresponding ground truth and process images with the trained deep neural network to determine objects.