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Yandong Wen
Researcher at Chinese Academy of Sciences
Publications - 39
Citations - 9510
Yandong Wen is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Facial recognition system & Convolutional neural network. The author has an hindex of 14, co-authored 37 publications receiving 7218 citations. Previous affiliations of Yandong Wen include South China University of Technology & Carnegie Mellon University.
Papers
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Book ChapterDOI
A Discriminative Feature Learning Approach for Deep Face Recognition
TL;DR: This paper proposes a new supervision signal, called center loss, for face recognition task, which simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers.
Proceedings ArticleDOI
SphereFace: Deep Hypersphere Embedding for Face Recognition
TL;DR: In this paper, the angular softmax (A-softmax) loss was proposed to learn angularly discriminative features for deep face recognition under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal interclass distance under a suitably chosen metric space.
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SphereFace: Deep Hypersphere Embedding for Face Recognition
TL;DR: This paper proposes the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features in deep face recognition (FR) problem under open-set protocol.
Proceedings Article
Large-margin softmax loss for convolutional neural networks
TL;DR: A generalized large-margin softmax (L-Softmax) loss which explicitly encourages intra-class compactness and inter-class separability between learned features and which not only can adjust the desired margin but also can avoid overfitting is proposed.
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Large-Margin Softmax Loss for Convolutional Neural Networks
TL;DR: In this article, a generalized large-margin softmax (L-Softmax) loss is proposed to encourage intra-class compactness and inter-class separability between learned features.