Nvidia patents AI method for smoother image generation
Nvidia describes a machine learning technique that creates new images by learning from existing ones. The system uses a model that adjusts its internal settings during training to ensure the generated images are smooth and consistent with the original data.
In the filing’s own words
One embodiment of the present invention sets forth a technique for performing machine learning. The technique includes inputting a training dataset into a variational autoencoder (VAE) comprising an encoder network, a prior network, and a decoder network. The technique also includes training the VAE by updating one or more parameters of the VAE based on a smoothness of one or more outputs produced by the VAE from the training dataset. The technique further includes producing generative output that reflects a first distribution of the training dataset by applying the decoder network to one or more values sampled from a second distribution of latent variables generated by the prior network.
What exactly they claim (claim 1)
1. A method for performing machine learning, comprising: inputting a set of training images into a machine learning model that comprises an encoder portion, a prior, and a decoder portion; training the machine learning model by updating one or more parameters of the machine learning model based on an objective function, the objective function controlling a smoothness of one or more outputs produced by the machine learning model when processing the set of training images, wherein training the machine learning model comprises: applying, to the objective function, a plurality of balancing coefficients to generate a modified objective function, the plurality of balancing coefficients including a first balancing coefficient that has a first value during a first training period and a second balancing coefficient that has a second value, wherein the second value is higher than the first value during a second training period that occurs after the first training period, and updating the one or more parameters of the machine learning model based on the modified objective function; and producing a new image that reflects one or more visual attributes associated with the set of training images by applying the decoder portion to a value generated based on an output of the prior.
Facts
- Company
- Nvidia (NVIDIA CORPORATION)
- Official title
- Deep hierarchical variational autoencoder
- Patent number
- US12737608B2
- Application number
- 17089492
- Filed
- Nov 4, 2020
- Granted
- Sep 15, 2026
- Inventors
- 2
- Claims
- 25
Classified under
G06N 3/08 Physics & computingG06N 3/04 Physics & computingG06N 3/0455 Physics & computingMore from Nvidia in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.