arXiv Open Access 2020

Improving Style-Content Disentanglement in Image-to-Image Translation

Aviv Gabbay Yedid Hoshen

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Abstrak

Unsupervised image-to-image translation methods have achieved tremendous success in recent years. However, it can be easily observed that their models contain significant entanglement which often hurts the translation performance. In this work, we propose a principled approach for improving style-content disentanglement in image-to-image translation. By considering the information flow into each of the representations, we introduce an additional loss term which serves as a content-bottleneck. We show that the results of our method are significantly more disentangled than those produced by current methods, while further improving the visual quality and translation diversity.

Topik & Kata Kunci

cs.CV cs.LG

Penulis (2)

Aviv Gabbay

Yedid Hoshen

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APA MLA BibTeX

Gabbay, A., Hoshen, Y. (2020). Improving Style-Content Disentanglement in Image-to-Image Translation. https://arxiv.org/abs/2007.04964

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓