Label Generation Using Neural Networks
AssigneeNVIDIA Corporation
InventorsZiyue Xu, Xiaosong Wang, Dong Yang, Holger Reinhard Roth, Can Zhao, Wentao Zhu, Daguang Xu
Life of this patent
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Abstract
Apparatuses, systems, and techniques to train one or more neural networks to generate labels for unsupervised or partially-supervised data. In at least one embodiment, one or more pseudolabels are generated by a training framework based on available weak annotations for an input medical image, and combined with feature information about said input medical image generated by one or more neural networks to generate a label about said input medical image.
Claims30
A processor comprising:
- one or more circuits to generate a labeled training image based, at least in part, on one or more objects in the training image determined by a neural network and one or more annotations associated with the training image.
A system comprising:
- one or more processors to generate a labeled training image based, at least in part, on one or more objects in the training image determined by a neural network and one or more annotations associated with the training image.
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 at least:
- generate a labeled training image based, at least in part, on one or more objects in the training image determined by a neural network and one or more annotations associated with the training image.
A method comprising:
- generating a labeled training image based, at least in part, on one or more objects in the training image determined by a neural network and one or more annotations associated with the training image.
Dependencies are inferred from the claim text.