Arcface: Additive angular margin loss for deep face recognition
One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, J Yang, N Xue, I Kotsia… - arXiv preprint arXiv …, 2018 - arxiv.org
Recently, a popular line of research in face recognition is adopting margins in the well-
established softmax loss function to maximize class separability. In this paper, we first …
established softmax loss function to maximize class separability. In this paper, we first …
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, J Yang, N Xue… - … on pattern analysis …, 2022 - pubmed.ncbi.nlm.nih.gov
Recently, a popular line of research in face recognition is adopting margins in the well-
established softmax loss function to maximize class separability. In this paper, we first …
established softmax loss function to maximize class separability. In this paper, we first …
[PDF][PDF] ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, N Xue, S Zafeiriou - ibug.doc.ic.ac.uk
One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for largescale face recognition is the design of appropriate loss functions that …
(DCNNs) for largescale face recognition is the design of appropriate loss functions that …
[PDF][PDF] ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, N Xue, S Zafeiriou - islab.ulsan.ac.kr
One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for largescale face recognition is the design of appropriate loss functions that can …
(DCNNs) for largescale face recognition is the design of appropriate loss functions that can …
[引用][C] Arcface: Additive angular margin loss for deep face recognition
J Deng - Proceedings of the IEEE Conference on Computer …, 2019 - cir.nii.ac.jp
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, J Yang, N Xue, I Kotsia… - IEEE Transactions on …, 2022 - dl.acm.org
Recently, a popular line of research in face recognition is adopting margins in the well-
established softmax loss function to maximize class separability. In this paper, we first …
established softmax loss function to maximize class separability. In this paper, we first …
[PDF][PDF] ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, N Xue, S Zafeiriou - researchgate.net
One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for largescale face recognition is the design of appropriate loss functions that …
(DCNNs) for largescale face recognition is the design of appropriate loss functions that …
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, J Yang, N Xue, I Kotsia… - IEEE Transactions on …, 2022 - computer.org
Recently, a popular line of research in face recognition is adopting margins in the well-
established softmax loss function to maximize class separability. In this paper, we first …
established softmax loss function to maximize class separability. In this paper, we first …
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
J Deng, J Guo, J Yang, N Xue, I Kotsia… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Recently, a popular line of research in face recognition is adopting margins in the well-
established softmax loss function to maximize class separability. In this paper, we first …
established softmax loss function to maximize class separability. In this paper, we first …