Back to results
US 2025/0028859 A1Published application
Protecting sensitive data associated with training machine learning models
AssigneeNVIDIA Corporation
InventorSean HUVER
Life of this patent
Estimating the term…
Abstract
Embodiments of the present disclosure relate to a method of performing one or more operations using a machine learning model. In some embodiments, the machine learning model may be trained, at least in part, using synthetic training data that may have been generated using one or more generative machine learning models. Further, the one or more generative machine learning models may be trained to generate the synthetic training data based at least on real training data designated for protection.
Claims20
A method comprising:
- performing one or more operations using a machine learning model, the machine learning model being trained, at least in part, using synthetic training data generated using one or more generative machine learning models, the one or more generative machine learning models being trained to generate the synthetic training data based at least on real training data designated for protection.
A system comprising:
- one or more processing units to perform operations comprising:
- performing one or more operations using a machine learning model, the machine learning model being trained, at least in part, using synthetic training data generated using one or more generative machine learning models, the one or more generative machine learning models being trained to generate the synthetic training data based at least on real training data designated for protection.
A processor comprising processing circuitry to perform operations, the operations comprising:
- performing one or more operations using a machine learning model, the machine learning model being trained, at least in part, using synthetic training data generated using one or more generative machine learning models, the one or more generative machine learning models being trained to generate the synthetic training data based at least on real training data designated for protection.
Dependencies are inferred from the claim text.