With the continuous development of face recognition network,the selection of loss function plays an increasingly important role in improving accuracy.The loss function of face recognition network needs to minimize the...With the continuous development of face recognition network,the selection of loss function plays an increasingly important role in improving accuracy.The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance.So far,one of our mainstream loss function optimization methods is to add penalty terms,such as orthogonal loss,to further constrain the original loss function.The other is to optimize using the loss based on angular/cosine margin.The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss.In this paper,based on the three methods with good practical performance and the joint optimization method,various loss functions are thoroughly reviewed.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.41875184)Innovation Team of“Six Talent Peaks”In Jiangsu Province(Grant No.TD-XYDXX-004).
文摘With the continuous development of face recognition network,the selection of loss function plays an increasingly important role in improving accuracy.The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance.So far,one of our mainstream loss function optimization methods is to add penalty terms,such as orthogonal loss,to further constrain the original loss function.The other is to optimize using the loss based on angular/cosine margin.The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss.In this paper,based on the three methods with good practical performance and the joint optimization method,various loss functions are thoroughly reviewed.
