{"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/squeeze-recover-and-relabel-dataset","title":"Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective","arxiv_id":"2306.13092","date":"2023-06-22","proceeding":"NeurIPS 2023 11","authors":["Zeyuan Yin","Eric Xing","Zhiqiang Shen"],"abstract":"We present a new dataset condensation framework termed Squeeze, Recover and Relabel (SRe$^2$L) that decouples the bilevel optimization of model and synthetic data during training, to handle varying scales of datasets, model architectures and image resolutions for efficient dataset condensation. The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with high-resolution synthesis, and the ability to scale up to arbitrary evaluation network architectures. Extensive experiments are conducted on Tiny-ImageNet and full ImageNet-1K datasets. Under 50 IPC, our approach achieves the highest 42.5% and 60.8% validation accuracy on Tiny-ImageNet and ImageNet-1K, outperforming all previous state-of-the-art methods by margins of 14.5% and 32.9%, respectively. Our approach also surpasses MTT in terms of speed by approximately 52$\\times$ (ConvNet-4) and 16$\\times$ (ResNet-18) faster with less memory consumption of 11.6$\\times$ and 6.4$\\times$ during data synthesis. Our code and condensed datasets of 50, 200 IPC with 4K recovery budget are available at https://github.com/VILA-Lab/SRe2L.","url_abs":"https://arxiv.org/abs/2306.13092v3","url_pdf":"https://arxiv.org/pdf/2306.13092v3.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":"squeeze-recover-and-relabel-dataset","repo_url":"https://github.com/VILA-Lab/SRe2L","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"squeeze-recover-and-relabel-dataset","repo_url":"https://github.com/shaoshitong/EDC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bilevel-optimization","task_name":"Bilevel Optimization"},{"task_slug":"dataset-condensation","task_name":"Dataset Condensation"},{"task_slug":"dataset-distillation","task_name":"Dataset Distillation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2306.13092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13092"}},"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/shaoshitong/EDC","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/VILA-Lab/SRe2L","reach":null}],"summary":{"ran_fixture":1,"unverified":5},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1},"listed":{"samples":3,"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":6,"samples":[{"code_sha256_prefix":"b6cec6dc0ac87904","entry":"denormalize","repo":"shaoshitong/EDC","repo_kind":"listed","path":"Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.py","file_url":"https://github.com/shaoshitong/EDC/blob/HEAD/Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.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":"b6cec6dc0ac87904"}},{"code_sha256_prefix":"cd717b4f9a335f2d","entry":"get_images","repo":"VILA-Lab/SRe2L","repo_kind":"official","path":"SRe2L/recover/data_synthesis.py","file_url":"https://github.com/VILA-Lab/SRe2L/blob/HEAD/SRe2L/recover/data_synthesis.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cd717b4f9a335f2d"}},{"code_sha256_prefix":"aa46aa8bd50d6cee","entry":"main_worker","repo":"shaoshitong/EDC","repo_kind":"listed","path":"Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.py","file_url":"https://github.com/shaoshitong/EDC/blob/HEAD/Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"aa46aa8bd50d6cee"}},{"code_sha256_prefix":"d8624e240350037c","entry":"save_images","repo":"shaoshitong/EDC","repo_kind":"listed","path":"Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.py","file_url":"https://github.com/shaoshitong/EDC/blob/HEAD/Branch_full_ImageNet_1k/recover/data_synthesis_without_optim.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d8624e240350037c"}},{"code_sha256_prefix":"4d9354edd6cfce15","entry":"save_images","repo":"VILA-Lab/SRe2L","repo_kind":"official","path":"SRe2L/recover/data_synthesis.py","file_url":"https://github.com/VILA-Lab/SRe2L/blob/HEAD/SRe2L/recover/data_synthesis.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4d9354edd6cfce15"}},{"code_sha256_prefix":"230fb2e9963063c9","entry":"validate","repo":"VILA-Lab/SRe2L","repo_kind":"official","path":"SRe2L/recover/data_synthesis.py","file_url":"https://github.com/VILA-Lab/SRe2L/blob/HEAD/SRe2L/recover/data_synthesis.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"230fb2e9963063c9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}