Invention Grant
- Patent Title: Attributionally robust training for weakly supervised localization and segmentation
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Application No.: US16926511Application Date: 2020-07-10
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Publication No.: US11544495B2Publication Date: 2023-01-03
- Inventor: Mayank Singh , Balaji Krishnamurthy , Nupur Kumari , Puneet Mangla
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Nicholson De Vos Webster & Elliott LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/62 ; G06N3/08 ; G06N3/04 ; G06T7/11

Abstract:
Embodiments are disclosed for training a neural network classifier to learn to more closely align an input image with its attribution map. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a training image comprising a representation of one or more objects, the training image associated with at least one label for the representation of the one or more objects, generating a perturbed training image based on the training image using a neural network, and training the neural network using the perturbed training image by minimizing a combination of classification loss and attribution loss to learn to align an image with its corresponding attribution map.
Public/Granted literature
- US20220012530A1 ATTRIBUTIONALLY ROBUST TRAINING FOR WEAKLY SUPERVISED LOCALIZATION AND SEGMENTATION Public/Granted day:2022-01-13
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