Invention Grant
- Patent Title: Attribution and generation of saliency visualizations for machine-learning models
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Application No.: US16719244Application Date: 2019-12-18
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Publication No.: US11755948B2Publication Date: 2023-09-12
- Inventor: Andrei Kapishnikov , Tolga Bolukbasi , Fernanda Bertini Viégas , Michael Andrew Terry
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: GOOGLE LLC
- Current Assignee: GOOGLE LLC
- Current Assignee Address: US CA Mountain View
- Agency: Dority & Manning, P.A.
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N5/04 ; G06N20/10

Abstract:
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.
Public/Granted literature
- US20210192382A1 Attribution and Generation of Saliency Visualizations for Machine-Learning Models Public/Granted day:2021-06-24
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