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It can learn a joint distribution with just samples drawn\nfrom the marginal distributions. This is achieved by enforcing a weight-sharing\nconstraint that limits the network capacity and favors a joint distribution\nsolution over a product of marginal distributions one. We apply CoGAN to\nseveral joint distribution learning tasks, including learning a joint\ndistribution of color and depth images, and learning a joint distribution of\nface images with different attributes. For each task it successfully learns the\njoint distribution without any tuple of corresponding images. We also\ndemonstrate its applications to domain adaptation and image transformation.","url_abs":"http://arxiv.org/abs/1606.07536v2","url_pdf":"http://arxiv.org/pdf/1606.07536v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"coupled-generative-adversarial-networks","repo_url":"https://github.com/mingyuliutw/CoGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"coupled-generative-adversarial-networks","repo_url":"https://github.com/Lornatang/CoGAN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"coupled-generative-adversarial-networks","repo_url":"https://github.com/eriklindernoren/Keras-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"coupled-generative-adversarial-networks","repo_url":"https://github.com/eriklindernoren/PyTorch-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-to-image-translation-on-cityscapes","task":"Image-to-Image Translation","dataset":"Cityscapes Labels-to-Photo","model":"CoGAN","rank_in_archive_order":18,"of":21,"metrics":{"Class IOU":"0.06","Per-class Accuracy":"10%","Per-pixel Accuracy":"40%"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-cityscapes-1","task":"Image-to-Image Translation","dataset":"Cityscapes Photo-to-Labels","model":"CoGAN","rank_in_archive_order":3,"of":5,"metrics":{"Class IOU":" 0.08","Per-class Accuracy":"11%","Per-pixel Accuracy":"45%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.07536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.07536"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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