Invention Grant
- Patent Title: Learning to generate synthetic datasets for training neural networks
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Application No.: US16685795Application Date: 2019-11-15
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Publication No.: US11610115B2Publication Date: 2023-03-21
- Inventor: Amlan Kar , Aayush Prakash , Ming-Yu Liu , David Jesus Acuna Marrero , Antonio Torralba Barriuso , Sanja Fidler
- Applicant: NVIDIA Corporation
- Applicant Address: US CA Santa Clara
- Assignee: NVIDIA Corporation
- Current Assignee: NVIDIA Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Taylor English Duma L.L.P.
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F16/901 ; G06T11/60 ; G06N3/04

Abstract:
In various examples, a generative model is used to synthesize datasets for use in training a downstream machine learning model to perform an associated task. The synthesized datasets may be generated by sampling a scene graph from a scene grammar—such as a probabilistic grammar—and applying the scene graph to the generative model to compute updated scene graphs more representative of object attribute distributions of real-world datasets. The downstream machine learning model may be validated against a real-world validation dataset, and the performance of the model on the real-world validation dataset may be used as an additional factor in further training or fine-tuning the generative model for generating the synthesized datasets specific to the task of the downstream machine learning model.
Public/Granted literature
- US20200160178A1 LEARNING TO GENERATE SYNTHETIC DATASETS FOR TRANING NEURAL NETWORKS Public/Granted day:2020-05-21
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