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Multi-task Adversarial Network for Disentangled Feature Learning

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Liu, Y 
Wang, Z 
Jin, H 


We address the problem of image feature learning for the applications where multiple factors exist in the image gen- eration process and only some factors are of our interest. We present a novel multi-task adversarial network based on an encoder-discriminator-generator architecture. The en- coder extracts a disentangled feature representation for the factors of interest. The discriminators classify each of the factors as individual tasks. The encoder and the discrimina- tors are trained cooperatively on factors of interest, but in an adversarial way on factors of distraction. The generator provides further regularization on the learned feature by re- constructing images with shared factors as the input image. We design a new optimization scheme to stabilize the ad- versarial optimization process when multiple distributions need to be aligned. The experiments on face recognition and font recognition tasks show that our method outper- forms the state-of-the-art methods in terms of both recog- nizing the factors of interest and generalization to images with unseen variations.



4603 Computer Vision and Multimedia Computation, 46 Information and Computing Sciences, 4611 Machine Learning

Journal Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Conference Name

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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