Back to results
US 2026/0087601 A1Published application
Denoising data using different diffusion neural networks
AssigneeNVIDIA Corporation
InventorsJames Robert Lucas, Jonathan Lorraine, Karsten Julian Kreis, Yanke Song, Weili Nie
Life of this patent
Estimating the term…
Abstract
Apparatuses, systems, and techniques to cause neural network inferencing or training data to be denoised are described. In at least one embodiment, denoising of neural network inferencing or training data may be performed based, at least in part, on identifying different types of inferencing or training data within the neural network inferencing or training data to be denoised separately using a corresponding number of diffusion neural networks.
Claims20
A processor, comprising:
- one or more circuits to cause neural network inferencing or training data to be denoised based, at least in part, on identifying different types of inferencing or training data within the neural network inferencing or training data to be denoised separately using a corresponding number of diffusion neural networks.
A method, comprising:
- causing neural network inferencing or training data to be denoised based, at least in part, on identifying different types of inferencing or training data within the neural network inferencing or training data to be denoised separately using a corresponding number of diffusion neural networks.
A system, comprising:
- one or more processors to cause neural network inferencing or training data to be denoised based, at least in part, on identifying different types of inferencing or training data within the neural network inferencing or training data to be denoised separately using a corresponding number of diffusion neural networks;
- and
- one or more memories to store weights of the diffusion neural networks.
Dependencies are inferred from the claim text.