Exploring Unlabeled Faces for Novel Attribute Discovery [article]

Hyojin Bahng, Sunghyo Chung, Seungjoo Yoo, Jaegul Choo
2019 arXiv   pre-print
Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images. This is a bottleneck for their real-world applications; in practice, a model trained on labeled CelebA dataset does not work well for test images from a different distribution -- greatly limiting their application to unlabeled images of a much larger quantity. In this paper, we attempt to alleviate this necessity for labeled data in the facial image translation
more » ... in. We aim to explore the degree to which you can discover novel attributes from unlabeled faces and perform high-quality translation. To this end, we use prior knowledge about the visual world as guidance to discover novel attributes and transfer them via a novel normalization method. Experiments show that our method trained on unlabeled data produces high-quality translations, preserves identity, and be perceptually realistic as good as, or better than, state-of-the-art methods trained on labeled data.
arXiv:1912.03085v1 fatcat:ylqfxvbyqbfwzmqzuosvrqx5vm