Neural network training using robust temporal ensembling
AssigneeNVIDIA Corporation
InventorsAbel Karl Brown, Robert Stephen DiPietro, Benedikt Dietmar Schifferer
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Abstract
Apparatuses, systems, and techniques to use one or more neural networks to generate data labels. In at least one embodiment, one or more neural networks is trained based, at least in part on, one or more labels, pseudo-labels, training data, and modified training data.
Claims37
A processor, comprising:
- one or more circuits to use one or more neural networks to generate data labels based, at least in part, on a first version of training data and a modified version of the first version of training data.
A system, comprising:
- one or more computers having one or more processors to train one or more neural networks to generate data labels based, at least in part, on a first version of training data and a modified version of the first version of training data.
A machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to train one or more neural networks to generate data labels based, at least in part, on a first version of training data and a modified version of the first version of training data.
A processor, comprising:
- one or more circuits to use one or more neural networks to generate data labels, wherein the one or more neural networks are trained based, at least in part, on a first version of training data and a modified version of the first version of training data.
A system, comprising:
- one or more computers having one or more processors to use one or more neural networks, wherein the one or more neural networks are trained to generate data labels based, at least in part, on a first version of training data and a modified version of the first version of training data.
A machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to use one or more neural networks to generate data labels, wherein the one or more neural networks are trained based, at least in part, on a first version of training data and a modified version of the first version of training data.
Dependencies are inferred from the claim text.