Deeply Learned Face Representations Are Sparse, Selective, and Robust
文献类型：会议论文
| 作者 | Yi Sun; Xiaogang Wang; Xiaoou Tang |
| 出版日期 | 2015 |
| 会议名称 | IEEE Conference on Computer Vision and Pattern Recognition |
| 会议地点 | 美国波士顿 |
| 英文摘要 | This paper designs a high-performance deep convolutional network (DeepID2+) for face recognition. It is learned with the identification-verification supervisory signal. By increasing the dimension of hidden representations and adding supervision to early convolutional layers, DeepID2+ achieves new state-of-the-art on LFW and YouTube Faces benchmarks. Through empirical studies, we have discovered three properties of its deep neural activations critical for the high performance: sparsity, selectiveness and robustness. (1) It is observed that neural activations are moderately sparse. Moderate sparsity maximizes the discriminative power of the deep net as well as the distance between images. It is surprising that DeepID2+ still can achieve high recognition accuracy even after the neural responses are binarized. (2) Its neurons in higher layers are highly selective to identities and identity-related attributes. We can identify different subsets of neurons which are either constantly excited or inhibited when different identities or attributes are present. Although DeepID2+ is not taught to distinguish attributes during training, it has implicitly learned such high-level concepts. (3) It is much more robust to occlusions, although occlusion patterns are not included in the training set. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6697]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2015 |
| 推荐引用方式 GB/T 7714 | Yi Sun,Xiaogang Wang,Xiaoou Tang. Deeply Learned Face Representations Are Sparse, Selective, and Robust[C]. 见:IEEE Conference on Computer Vision and Pattern Recognition. 美国波士顿. |
入库方式： OAI收割
来源：深圳先进技术研究院
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