2019/11/02 by Weihao Xia, Yujiu Yang, Xia, Weihao +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Image Processing Techniques #Cancer-related molecular mechanisms research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis
paper · pdf · doi:10.48550/arxiv.1911.00622
openalex publication_date 2019/11/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Image-to-image translation has drawn great attention during the past few years. It aims to translate an image in one domain to a given reference image in another domain. Due to its effectiveness and efficiency, many applications can be formulated as image-to-image translation problems. However, three main challenges remain in image-to-image translation: 1) the lack of large amounts of aligned training pairs for different tasks; 2) the ambiguity of multiple possible outputs from a single input image; and 3) the lack of simultaneous training of multiple datasets from different domains within a single network. We also found in experiments that the implicit disentanglement of content and style could lead to unexpect results. In this paper, we propose a unified framework for learning to generate diverse outputs using unpaired training data and allow simultaneous training of multiple datasets from different domains via a single network. Furthermore, we also investigate how to better extract domain supervision information so as to learn better disentangled representations and achieve better image translation. Experiments show that the proposed method outperforms or is comparable with the state-of-the-art methods.