-
公开(公告)号:US20220012530A1
公开(公告)日:2022-01-13
申请号:US16926511
申请日:2020-07-10
Applicant: Adobe Inc.
Inventor: Mayank SINGH , Balaji Krishnamurthy , Nupur KUMARI , Puneet MANGLA
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.
-
公开(公告)号:US20230139927A1
公开(公告)日:2023-05-04
申请号:US18148256
申请日:2022-12-29
Applicant: Adobe Inc.
Inventor: Mayank SINGH , Balaji Krishnamurthy , Nupur KUMARI , Puneet MANGLA
IPC: G06T7/11 , G06N3/08 , G06N3/04 , G06F18/214 , G06F18/21 , G06V10/774 , G06V10/82
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.
-