发明授权
- 专利标题: Attribution and generation of saliency visualizations for machine-learning models
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申请号: US16719244申请日: 2019-12-18
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公开(公告)号: US11755948B2公开(公告)日: 2023-09-12
- 发明人: Andrei Kapishnikov , Tolga Bolukbasi , Fernanda Bertini Viégas , Michael Andrew Terry
- 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: Dority & Manning, P.A.
- 主分类号: G06N20/00
- IPC分类号: G06N20/00 ; G06N5/04 ; G06N20/10
摘要:
Methods, systems, devices, and tangible non-transitory computer readable media for saliency visualization are provided. The disclosed technology can include receiving a data input including a plurality of features. The data input can be segmented into regions. At least one of the regions can include two or more of the features. Attribution scores can be respectively generated for features of the data input. The attribution scores for each feature can be indicative of a respective saliency of such feature. A respective gain value for each region can be determined over one or more iterations based on the respective attribution scores associated with the features included in the region. Further, at each iteration one or more of the regions with the greatest gain values can be added to a saliency mask. Furthermore, at each iteration a saliency visualization can be produced based on the saliency mask.
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