{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/co-evolutionary-compression-for-unpaired","title":"Co-Evolutionary Compression for Unpaired Image Translation","arxiv_id":"1907.10804","date":"2019-07-25","proceeding":"ICCV 2019 10","authors":["Han Shu","Yunhe Wang","Xu Jia","Kai Han","Hanting Chen","Chunjing Xu","Qi Tian","Chang Xu"],"abstract":"Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation. However, generators in these networks are of complicated architectures with large number of parameters and huge computational complexities. Existing methods are mainly designed for compressing and speeding-up deep neural networks in the classification task, and cannot be directly applied on GANs for image translation, due to their different objectives and training procedures. To this end, we develop a novel co-evolutionary approach for reducing their memory usage and FLOPs simultaneously. In practice, generators for two image domains are encoded as two populations and synergistically optimized for investigating the most important convolution filters iteratively. Fitness of each individual is calculated using the number of parameters, a discriminator-aware regularization, and the cycle consistency. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method for obtaining compact and effective generators.","url_abs":"https://arxiv.org/abs/1907.10804v1","url_pdf":"https://arxiv.org/pdf/1907.10804v1.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":"co-evolutionary-compression-for-unpaired","repo_url":"https://github.com/huawei-noah/Pruning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"co-evolutionary-compression-for-unpaired","repo_url":"https://github.com/yehuitang/Pruning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1907.10804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10804"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/huawei-noah/Pruning","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yehuitang/Pruning","reach":null}],"summary":{"ran_fixture":3},"by_repo_kind":{"listed":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"7e8fb3ba0c6d19a2","entry":"crossover","repo":"yehuitang/Pruning","repo_kind":"listed","path":"GAN-Pruning/GA.py","file_url":"https://github.com/yehuitang/Pruning/blob/HEAD/GAN-Pruning/GA.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7e8fb3ba0c6d19a2"}},{"code_sha256_prefix":"69c9e2133b343749","entry":"mutation","repo":"yehuitang/Pruning","repo_kind":"listed","path":"GAN-Pruning/GA.py","file_url":"https://github.com/yehuitang/Pruning/blob/HEAD/GAN-Pruning/GA.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"69c9e2133b343749"}},{"code_sha256_prefix":"62f6c77a14287f9c","entry":"roulette","repo":"yehuitang/Pruning","repo_kind":"listed","path":"GAN-Pruning/GA.py","file_url":"https://github.com/yehuitang/Pruning/blob/HEAD/GAN-Pruning/GA.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"62f6c77a14287f9c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}