Multi-task Adversarial Network for Disentangled Feature Learning

We address the problem of image feature learning for the applications where multiple factors exist in the image generation 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 encoder extrac...

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Veröffentlicht in:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition S. 3743 - 3751
Hauptverfasser: Liu, Yang, Wang, Zhaowen, Jin, Hailin, Wassell, Ian
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2018
Schlagworte:
Feature extraction
Gallium nitride
Generators
Image generation
Optimization
Task analysis
Training
ISSN:1063-6919
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Zusammenfassung:We address the problem of image feature learning for the applications where multiple factors exist in the image generation 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 encoder extracts a disentangled feature representation for the factors of interest. The discriminators classify each of the factors as individual tasks. The encoder and the discriminators 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 reconstructing images with shared factors as the input image. We design a new optimization scheme to stabilize the adversarial optimization process when multiple distributions need to be aligned. The experiments on face recognition and font recognition tasks show that our method outperforms the state-of-the-art methods in terms of both recognizing the factors of interest and generalization to images with unseen variations.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00394