Nvidia Corporation

Wentao Zhu

Daguang Xu

Andrew Feng

Yan Cheng

Xiaosong Wang

Wenqi Li

Fausto Milletari

Liyue Shen

Nicola Christin Rieke

Rong Ou

Charles Jonathan Hancox

Apparatuses, systems, and techniques to generate one or more data items related to an object from a set of multiple data items related to the same object using a generative adversarial network. In at least one embodiment, data objects in a set are disentangled into common and unique components and encoded such that they may be used to train one or more neural networks to generate missing data from the data objects in the set, such as generating missing medical images from a set of related medical images.

Representational disentanglement for multi-modality image completion using neural networks

Apparatuses, systems, and techniques to perform federated training of neural networks while maintaining control over dissemination of local models of neural networks from which aspects of local training data might be extracted. In at least one embodiment, a neural network is trained on local training data and a local model is provided to be aggregated with other local models into a global model that is in turn used for further local model training, wherein a provided local model or training is adjusted to reduce an ability to extract aspects of local training data therefrom.

Neural network trained using federated learning with local training data preserved at local edge circuits

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Mountain View, CA, United States of America

Wentao Zhu