Abstract
Recent studies have shown significant advance for multi-domain image-to-image translation, and generative adversarial networks (GANs) are widely used to address this problem. However, to train an effective image generator, existing methods all require a large number of domain-labeled images, which may take time and effort to collect for real-world problems. In this paper, we propose SemiStarGAN, a semi-supervised GAN network to tackle this issue. The proposed method utilizes unlabeled images by incorporating a novel discriminator/classifier network architecture—Y model, and two existing semi-supervised learning techniques—pseudo labeling and self-ensembling. Experimental results on the CelebA dataset using domains of facial attributes show that the proposed method achieves comparable performance with state-of-the-art methods using considerably less labeled training images.
This research was supported in part by the Ministry of Science and Technology of Taiwan (MOST 107-2633-E-002-001, 106-2218-E-002-043, 107-2811-E-002-018), National Taiwan University (NTU-107L104039), Intel Corporation, and Delta Electronics.
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Hsu, SY., Yang, CY., Huang, CC., Hsu, J.Yj. (2019). SemiStarGAN: Semi-supervised Generative Adversarial Networks for Multi-domain Image-to-Image Translation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_21
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