Abstract: Apparatuses, systems, and techniques to perform active learning.

Abstract: A device receives a two-dimensional (2-D) image that depicts a cross-sectional view of a macula comprised of layers and boundaries to segment the layers, and determines spatial coordinates of the 2-D image that include x-coordinates and y-coordinates. The device uses a data model, that has been trained using a deep learning technique, to process the 2-D image and the spatial coordinates to generate boundary maps that indicate likelihoods of voxels of the 2-D image being in positions that are part of particular boundaries. The device determines, by analyzing the boundary maps, an initial set of boundary positions, and determines a final set of boundary positions by using a topological order identification technique to refine the initial set of boundary positions. The device determines the thickness levels of the layers of the macula based on the final set of boundary positions, and performs one or more actions based on the thickness levels.

Abstract: Apparatuses, systems, and techniques to generate one or more neural networks. In at least one embodiment, one or more neural networks are generated, based on, for example, one or more convolutional neural network operations and one or more transformer neural network operations.

Abstract: Apparatuses, systems, and techniques are presented to generate images representing realistic motion or activity. In at least one embodiment, one or more neural networks are used to select a first neural network to perform a first task based, at least in part, upon a performance estimated by a second neural network.

Abstract: A wearable blanket includes a blanket body. The blanket body includes a main body portion configured to wrap a trunk of a human body, a cap portion configured to wrap a head of the human body, and two sleeve portions configured to wrap arms of the human body. The cap portion is connected to a top hem of the main body portion. The sleeve portions are respectively connected to a left side and a right side of the main body portion. At least one partial area of the outer surface of the blanket body is coated with a fluorescent material. An outer surface of the blanket body defines a plurality of patterned areas. At least one of the plurality of patterned areas is coated with the fluorescent material.

Abstract: Techniques for retinal layer segmentation are presented. The techniques include obtaining current optical coherence tomography (OCT) data for a retina; generating an estimated current en face image based on the current OCT data and an estimated current retinal layer segmentation; determining a registered previous retinal layer segmentation based on a previous retinal layer segmentation, a previous en face image, the estimated current en face image, and the estimated current retinal layer segmentation; updating the estimated current retinal layer segmentation using a deep neural network and based on the current OCT data and the registered previous retinal layer segmentation; repeating the generating, the determining, and the updating to obtain a current retinal layer segmentation as the estimated current retinal layer segmentation; and outputting a property of the retina determined at least in part from the current retinal layer segmentation.

Abstract: Apparatuses, systems, and techniques to select a neural network using an amount of memory to be used. In at least one embodiment, a processor includes one or more circuits to cause one or more neural networks to be selected from a plurality of neural networks based, at least in part, on an amount of memory to be used by the one oe more neural networks.

Abstract: A device receives a two-dimensional (2-D) image that depicts a cross-sectional view of a macula comprised of layers and boundaries to segment the layers, and determines spatial coordinates of the 2-D image that include x-coordinates and y-coordinates. The device uses a data model, that has been trained using a deep learning technique, to process the 2-D image and the spatial coordinates to generate boundary maps that indicate likelihoods of voxels of the 2-D image being in positions that are part of particular boundaries. The device determines, by analyzing the boundary maps, an initial set of boundary positions, and determines a final set of boundary positions by using a topological order identification technique to refine the initial set of boundary positions. The device determines the thickness levels of the layers of the macula based on the final set of boundary positions, and performs one or more actions based on the thickness levels.