公开(公告)号：US11977602B2

公开(公告)日：2024-05-07

申请号：US17521252

申请日：2021-11-08

Applicant: NEC Laboratories America, Inc.

Inventor： Xiang Yu ,  Yi-Hsuan Tsai ,  Masoud Faraki ,  Ramin Moslemi ,  Manmohan Chandraker ,  Chang Liu

IPC: G06K9/00 ,  G06F18/21 ,  G06F18/214 ,  G06N20/00 ,  G06V40/16

CPC classification number: G06F18/214 ,  G06F18/217 ,  G06N20/00 ,  G06V40/172

Abstract: A method for training a model for face recognition is provided. The method forward trains a training batch of samples to form a face recognition model w(t), and calculates sample weights for the batch. The method obtains a training batch gradient with respect to model weights thereof and updates, using the gradient, the model w(t) to a face recognition model what(t). The method forwards a validation batch of samples to the face recognition model what(t). The method obtains a validation batch gradient, and updates, using the validation batch gradient and what(t), a sample-level importance weight of samples in the training batch to obtain an updated sample-level importance weight. The method obtains a training batch upgraded gradient based on the updated sample-level importance weight of the training batch samples, and updates, using the upgraded gradient, the model w(t) to a trained model w(t+1) corresponding to a next iteration.

公开(公告)号：US20220147767A1

公开(公告)日：2022-05-12

申请号：US17521252

申请日：2021-11-08

Applicant: NEC Laboratories America, Inc.

Inventor： Xiang Yu ,  Yi-Hsuan Tsai ,  Masoud Faraki ,  Ramin Moslemi ,  Manmohan Chandraker ,  Chang Liu

IPC: G06K9/62 ,  G06N20/00 ,  G06K9/00

Abstract: A method for training a model for face recognition is provided. The method forward trains a training batch of samples to form a face recognition model w(t), and calculates sample weights for the batch. The method obtains a training batch gradient with respect to model weights thereof and updates, using the gradient, the model w(t) to a face recognition model what(t). The method forwards a validation batch of samples to the face recognition model what(t). The method obtains a validation batch gradient, and updates, using the validation batch gradient and what(t), a sample-level importance weight of samples in the training batch to obtain an updated sample-level importance weight. The method obtains a training batch upgraded gradient based on the updated sample-level importance weight of the training batch samples, and updates, using the upgraded gradient, the model w(t) to a trained model w(t+1) corresponding to a next iteration.