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Collaborative Learning Network for Face Attribute Prediction

  • Shiyao Wang
  • Zhidong DengEmail author
  • Zhenyang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

This paper proposes a facial attributes learning algorithm with deep convolutional neural networks (CNN). Instead of jointly predicting all the facial attributes (40 attributes in our case) with a shared CNN feature extraction hierarchy, we cluster the facial attributes into groups and the CNN only shares features within each group in later feature extraction stages to jointly predicts the attributes in each group respectively. This paper also proposes a simple yet effective attribute clustering algorithm, based on the observation that some attributes are more collaborated (their prediction accuracy improve more when jointly learned) than others, and the proposed deep network is referred to as the collaborative learning network. Contrary to the previous state-of-the-art facial attribute recognition methods which require pre-training on external datasets, the proposed collaborative learning network is trained for attribute recognition from scratch without external data while achieving the best attribute recognition accuracy on the challenging CelebA dataset and the second best on the LFW dataset.

Keywords

Convolutional Neural Network Stochastic Gradient Descent Attribute Recognition Facial Attribute Pairwise Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

The authors would like to thank the anonymous reviewers for their valuable comments that considerably contributed to improving this paper. This work was supported in part by the National Science Foundation of China (NSFC) under Grant Nos. 91420106, 90820305, and 60775040, and by the National High-Tech R&D Program of China under Grant No. 2012AA041402.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer ScienceTsinghua UniversityBeijingChina

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