{"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/ugc-unified-gan-compression-for-efficient","title":"UGC: Unified GAN Compression for Efficient Image-to-Image Translation","arxiv_id":"2309.09310","date":"2023-09-17","proceeding":"ICCV 2023 1","authors":["Yuxi Ren","Jie Wu","Peng Zhang","Manlin Zhang","Xuefeng Xiao","Qian He","Rui Wang","Min Zheng","Xin Pan"],"abstract":"Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii)data/label-efficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN Compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model.","url_abs":"https://arxiv.org/abs/2309.09310v1","url_pdf":"https://arxiv.org/pdf/2309.09310v1.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":[],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.09310","atlas_url":"https://app.syntology.ai/?focus=2309.09310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.09310"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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":"deterministic:regex_extraction","url":"https://github.com/Tencent/TNN","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"found_in_text":{"samples":2,"ran":1,"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":2,"samples":[{"code_sha256_prefix":"231dd33f186caadd","entry":"convert_string_to_hex_list","repo":"Tencent/TNN","repo_kind":"found_in_text","path":"source/tnn/device/opencl/cl/opencl_codegen.py","file_url":"https://github.com/Tencent/TNN/blob/HEAD/source/tnn/device/opencl/cl/opencl_codegen.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"231dd33f186caadd"}},{"code_sha256_prefix":"7afaca6da81652c0","entry":"optimize_graph","repo":"Tencent/TNN","repo_kind":"found_in_text","path":"tutorial/mobilenet_v2_ssd/code/part2_clean_tensorflow_model.py","file_url":"https://github.com/Tencent/TNN/blob/HEAD/tutorial/mobilenet_v2_ssd/code/part2_clean_tensorflow_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"7afaca6da81652c0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}