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公开(公告)号：US11899749B2
公开(公告)日：2024-02-13
申请号：US17201816
申请日：2021-03-15
Applicant: NVIDIA Corporation
Inventor： Subhashree Radhakrishnan , Partha Sriram , Farzin Aghdasi , Seunghwan Cha , Zhiding Yu
CPC classification number: G06F18/214 , G06T3/0006 , G06T7/12 , G06V10/22 , G06V10/242 , G06V20/40 , G06T2207/20081 , G06T2207/20084
Abstract: In various examples, training methods as described to generate a trained neural network that is robust to various environmental features. In an embodiment, training includes modifying images of a dataset and generating boundary boxes and/or other segmentation information for the modified images which is used to train a neural network.
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公开(公告)号：US20210089921A1
公开(公告)日：2021-03-25
申请号：US17029725
申请日：2020-09-23
Applicant: Nvidia Corporation
Inventor： Farzin Aghdasi , Varun Praveen , FNU Ratnesh Kumar , Partha Sriram
Abstract: Transfer learning can be used to enable a user to obtain a machine learning model that is fully trained for an intended inferencing task without having to train the model from scratch. A pre-trained model can be obtained that is relevant for that inferencing task. Additional training data, as may correspond to at least one additional class of data, can be used to further train this model. This model can then be pruned and retrained in order to obtain a smaller model that retains high accuracy for the intended inferencing task.
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公开(公告)号：US20220292306A1
公开(公告)日：2022-09-15
申请号：US17201816
申请日：2021-03-15
Applicant: NVIDIA Corporation
Inventor： Subhashree Radhakrishnan , Partha Sriram , Farzin Aghdasi , Seunghwan Cha , Zhiding Yu
Abstract: In various examples, training methods as described to generate a trained neural network that is robust to various environmental features. In an embodiment, training includes modifying images of a dataset and generating boundary boxes and/or other segmentation information for the modified images which is used to train a neural network.
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