{"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/are-large-scale-soft-labels-necessary-for","title":"Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?","arxiv_id":"2410.15919","date":"2024-10-21","proceeding":null,"authors":["Lingao Xiao","Yang He"],"abstract":"In ImageNet-condensation, the storage for auxiliary soft labels exceeds that of the condensed dataset by over 30 times. However, are large-scale soft labels necessary for large-scale dataset distillation? In this paper, we first discover that the high within-class similarity in condensed datasets necessitates the use of large-scale soft labels. This high within-class similarity can be attributed to the fact that previous methods use samples from different classes to construct a single batch for batch normalization (BN) matching. To reduce the within-class similarity, we introduce class-wise supervision during the image synthesizing process by batching the samples within classes, instead of across classes. As a result, we can increase within-class diversity and reduce the size of required soft labels. A key benefit of improved image diversity is that soft label compression can be achieved through simple random pruning, eliminating the need for complex rule-based strategies. Experiments validate our discoveries. For example, when condensing ImageNet-1K to 200 images per class, our approach compresses the required soft labels from 113 GB to 2.8 GB (40x compression) with a 2.6% performance gain. Code is available at: https://github.com/he-y/soft-label-pruning-for-dataset-distillation","url_abs":"https://arxiv.org/abs/2410.15919v2","url_pdf":"https://arxiv.org/pdf/2410.15919v2.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":"are-large-scale-soft-labels-necessary-for","repo_url":"https://github.com/he-y/soft-label-pruning-for-dataset-distillation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dataset-distillation","task_name":"Dataset Distillation"},{"task_slug":"diversity","task_name":"Diversity"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.15919","atlas_url":"https://app.syntology.ai/?focus=2410.15919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15919"}},"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/he-y/soft-label-pruning-for-dataset-distillation","reach":null}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"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":"66e243b18e3b93cf","entry":"get_images","repo":"he-y/soft-label-pruning-for-dataset-distillation","repo_kind":"official","path":"recover/data_synthesis_class.py","file_url":"https://github.com/he-y/soft-label-pruning-for-dataset-distillation/blob/HEAD/recover/data_synthesis_class.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":"66e243b18e3b93cf"}},{"code_sha256_prefix":"b9be5012db1c17e9","entry":"save_images","repo":"he-y/soft-label-pruning-for-dataset-distillation","repo_kind":"official","path":"recover/data_synthesis_class.py","file_url":"https://github.com/he-y/soft-label-pruning-for-dataset-distillation/blob/HEAD/recover/data_synthesis_class.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":"b9be5012db1c17e9"}},{"code_sha256_prefix":"5b12dc9b2a14d118","entry":"validate","repo":"he-y/soft-label-pruning-for-dataset-distillation","repo_kind":"official","path":"recover/data_synthesis_class.py","file_url":"https://github.com/he-y/soft-label-pruning-for-dataset-distillation/blob/HEAD/recover/data_synthesis_class.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":"5b12dc9b2a14d118"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}