University of California

Nvidia Corporation

Ali Hatamizadeh

Daguang Xu

Jonathan Hopkins

Xiaosong Wang

Wenqi Li

Demetri Terzopoulos

Yuanping Song

Lickkong Tam

Yuhong Wen

Riddhish Bhalodia

Can Zhao

Kevin Frank Lu

Apparatuses, systems, and techniques to generate labeled training data. In at least one embodiment, labeled training images are generated from medial images annotated with natural language text.

Extending supervision using machine learning

A boundary learning optimization tool for training neural networks with accurate models of parameterized flexure using a minimal number of numerically generated performance solutions generated from different design instantiations of those topologies. Performance boundaries are output by the neural network in optimization steps, with geometric parameters varied from smallest allowable feature sizes to largest geometrically compatible feature sizes for given constituent materials. The plotted performance boundaries define the design spaces of flexure systems toward allowing designers to visually identify which geometric versions of their synthesized topologies best achieve desired combinations of performance capabilities.

Apparatus and method for boundary learning optimization

Systems and methods for image segmentation using neural networks and active contour methods in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating image segmentations from an input image. The method includes steps for receiving an input image, identifying a set of one or more parameter maps from the input image, identifying an initialization map from the input image, and generating an image segmentation based on the set of parameter maps and the initialization map.

Systems and methods for trainable deep active contours for image segmentation

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Los Angeles, CA, United States of America

Ali Hatamizadeh