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    LEARNING TO GENERATE SYNTHETIC DATASETS FOR TRANING NEURAL NETWORKS
    3.
    发明申请
    LEARNING TO GENERATE SYNTHETIC DATASETS FOR TRANING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200160178A1

    公开(公告)日：2020-05-21

    申请号：US16685795

    申请日：2019-11-15

    Applicant: NVIDIA Corporation

    Inventor： Amlan Kar ,  Aayush Prakash ,  Ming-Yu Liu ,  David Jesus Acuna Marrero ,  Antonio Torralba Barriuso ,  Sanja Fidler

    IPC: G06N3/08 ,  G06F16/901 ,  G06N3/04 ,  G06T11/60

    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.

    Learning to generate synthetic datasets for training neural networks
    7.
    发明授权
    Learning to generate synthetic datasets for training neural networks 有权

    公开(公告)号：US11610115B2

    公开(公告)日：2023-03-21

    申请号：US16685795

    申请日：2019-11-15

    Applicant: NVIDIA Corporation

    Inventor： Amlan Kar ,  Aayush Prakash ,  Ming-Yu Liu ,  David Jesus Acuna Marrero ,  Antonio Torralba Barriuso ,  Sanja Fidler

    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.

    ITERATIVE SPATIAL GRAPH GENERATION
    9.
    发明申请
    ITERATIVE SPATIAL GRAPH GENERATION 审中-公开

    公开(公告)号：US20200302250A1

    公开(公告)日：2020-09-24

    申请号：US16825199

    申请日：2020-03-20

    Applicant: Nvidia Corporation

    Inventor： Hang Chu ,  Daiqing Li ,  David Jesus Acuna Marrero ,  Amlan Kar ,  Maria Shugrina ,  Ming-Yu Liu ,  Antonio Torralba Barriuso ,  Sanja Fidler

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20

    Abstract: A generative model can be used for generation of spatial layouts and graphs. Such a model can progressively grow these layouts and graphs based on local statistics, where nodes can represent spatial control points of the layout, and edges can represent segments or paths between nodes, such as may correspond to road segments. A generative model can utilize an encoder-decoder architecture where the encoder is a recurrent neural network (RNN) that encodes local incoming paths into a node and the decoder is another RNN that generates outgoing nodes and edges connecting an existing node to the newly generated nodes. Generation is done iteratively, and can finish once all nodes are visited or another end condition is satisfied. Such a model can generate layouts by additionally conditioning on a set of attributes, giving control to a user in generating the layout.

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