- 专利标题: Learning compressible features
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申请号: US16666689申请日: 2019-10-29
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公开(公告)号: US11610124B2公开(公告)日: 2023-03-21
- 发明人: Abhinav Shrivastava , Saurabh Singh , Johannes Balle , Sami Ahmad Abu-El-Haija , Nicholas Johnston , George Dan Toderici
- 申请人: Google LLC
- 申请人地址: US CA Mountain View
- 专利权人: Google LLC
- 当前专利权人: Google LLC
- 当前专利权人地址: US CA Mountain View
- 代理机构: Brake Hughes Bellermann LLP
- 主分类号: G06K9/00
- IPC分类号: G06K9/00 ; G06N3/082 ; G06F17/15 ; G06K9/62 ; G06N3/063
摘要:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, by a neural network (NN), a dataset for generating features from the dataset. A first set of features is computed from the dataset using at least a feature layer of the NN. The first set of features i) is characterized by a measure of informativeness; and ii) is computed such that a size of the first set of features is compressible into a second set of features that is smaller in size than the first set of features and that has a same measure of informativeness as the measure of informativeness of the first set of features. The second set of features if generated from the first set of features using a compression method that compresses the first set of features to generate the second set of features.
公开/授权文献
- US20200311548A1 LEARNING COMPRESSIBLE FEATURES 公开/授权日:2020-10-01
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