Abstract: Techniques for coding sets of images with neural networks include transforming a first image of a set of images into coefficients with an encoder neural network, encoding a group of the coefficients as an integer patch index into coding table of table entries each having vectors of coefficients, and storing a collection of patch indices as a first coded image. The encoder neural network may be configured with encoder weights determined by jointly with corresponding decoder weights of a decoder neural network on the set of images.

Abstract: Implementations of the subject technology relate to generative scene networks (GSNs) that are able to generate realistic scenes that can be rendered from a free moving camera at any location and orientation. A GSN may be implemented using a global generator and a locally conditioned radiance field. GSNs may employ a spatial latent representation as conditioning for a grid of locally conditioned radiance fields, and may be trained using an adversarial learning framework. Inverting a GSN may allow free navigation of a generated scene conditioned on one or more observations.

Abstract: Attention-free transformers are disclosed. Various implementations of attention-free transformers include a gating and pooling operation that allows the attention-free transformers to provide comparable or better results to those of a standard attention-based transformer, with improved efficiency and reduced computational complexity with respect to space and time.

Abstract: Feature descriptor matching is reformulated into a graph-matching problem. Keypoints from a query image and a reference image are initially matched and filtered based on the match. For a given keypoint, a feature graph is constructed based on neighboring keypoints surrounding the given keypoint. The feature graph is compared to a corresponding feature graph of a reference image for the matched keypoint. Relocalization data is obtained based on the comparison.

Abstract: Systems and processes for metric learning distillation are disclosed herein. In accordance with one example, a method includes, at an electronic device, at an electronic device having one or more processors and memory, receiving a first plurality of vectors from a first model, receiving a second plurality of vectors from a second model, determining a first plurality of vector distances based on the first plurality of vectors, generating a first matrix based on the first plurality of vector distances, determining a second plurality of vector distances based on the second plurality of vectors, generating a second matrix based on the second plurality of vector distances, comparing the first matrix with the second matrix, and adjusting the second model based on the comparison of the first matrix and the second matrix.

Abstract: A facial recognition authentication process operating on a device may capture an image of a user using a camera on the device. The facial recognition authentication process may include operating a full face facial recognition authentication process on the captured image or operating a partial face facial recognition authentication process on the captured image. The process may determine which process to operate (either full face or partial face) based on an assessment of an amount of occlusion in the captured image. The partial face facial recognition authentication process may be operated when there is at least some occlusion of selected features (e.g., nose and/or mouth) on the user's face in the captured image.

Abstract: A facial recognition authentication process may utilize images of a user's face that are captured while the user is being illuminated using both flood infrared illumination and patterned illumination (e.g., speckle pattern illumination). As the user's face is illuminated by both flood infrared illumination and patterned illumination, the captured images may include both flood infrared illumination data and depth map image data. Flood infrared illumination data may be generated from the images to assess two-dimensional features of the user in the captured images. Depth map image data may be generated from the pattern data in the images to assess three-dimensional (depth) features of the user in the captured images. The flood infrared illumination data and the depth map image data may be used separately by facial recognition authentication process to attempt to authenticate the user in the captured images as an authorized user of the device.

Abstract: Techniques for coding sets of images with neural networks include transforming a first image of a set of images into coefficients with an encoder neural network, encoding a group of the coefficients as an integer patch index into coding table of table entries each having vectors of coefficients, and storing a collection of patch indices as a first coded image. The encoder neural network may be configured with encoder weights determined by jointly with corresponding decoder weights of a decoder neural network on the set of images.

Abstract: The subject technology trains, for a first set of iterations, a first machine learning model using a loss function with a first set of parameters. The subject technology determines, by a second machine learning model, a state of the first machine learning model corresponding to the first set of iterations. The subject technology determines, by the second machine learning model, an action for updating the loss function based on the state of the first machine learning model. The subject technology updates, by the second machine learning model, the loss function based at least in part on the action, where the updated loss function includes a second set of parameters corresponding to a change in values of the first set of parameters. The subject technology trains, for a second set of iterations, the first machine learning model using the updated loss function with the second set of parameters.

Abstract: A facial recognition authentication process operating on a device may capture an image of a user using a camera on the device. The facial recognition authentication process may include operating a full face facial recognition authentication process on the captured image or operating a partial face facial recognition authentication process on the captured image. The process may determine which process to operate (either full face or partial face) based on an assessment of an amount of occlusion in the captured image. The partial face facial recognition authentication process may be operated when there is at least some occlusion of selected features (e.g., nose and/or mouth) on the user's face in the captured image.

Abstract: A facial recognition authentication process may utilize images of a user's face that are captured while the user is being illuminated using both flood infrared illumination and patterned illumination (e.g., speckle pattern illumination). As the user's face is illuminated by both flood infrared illumination and patterned illumination, the captured images may include both flood infrared illumination data and depth map image data. Flood infrared illumination data may be generated from the images to assess two-dimensional features of the user in the captured images. Depth map image data may be generated from the pattern data in the images to assess three-dimensional (depth) features of the user in the captured images. The flood infrared illumination data and the depth map image data may be used separately by facial recognition authentication process to attempt to authenticate the user in the captured images as an authorized user of the device.

Abstract: Systems and processes for metric learning distillation are disclosed herein. In accordance with one example, a method includes, at an electronic device, at an electronic device having one or more processors and memory, receiving a first plurality of vectors from a first model, receiving a second plurality of vectors from a second model, determining a first plurality of vector distances based on the first plurality of vectors, generating a first matrix based on the first plurality of vector distances, determining a second plurality of vector distances based on the second plurality of vectors, generating a second matrix based on the second plurality of vector distances, comparing the first matrix with the second matrix, and adjusting the second model based on the comparison of the first matrix and the second matrix.

Abstract: Machine learning systems are represented as directed acyclic graphs, where the nodes represent functional modules in the system and edges represent input/output relations between the functional modules. A machine learning environment can then be created to facilitate the training and operation of these machine learning systems.