{"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/rethinking-the-representational-continuity-1","title":"Representational Continuity for Unsupervised Continual Learning","arxiv_id":"2110.06976","date":"2021-10-13","proceeding":"ICLR 2022 4","authors":["Divyam Madaan","Jaehong Yoon","Yuanchun Li","Yunxin Liu","Sung Ju Hwang"],"abstract":"Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual learning (SCL) scenarios. Consequently, they are not scalable to real-world applications where the data distribution is often biased and unannotated. In this work, we focus on unsupervised continual learning (UCL), where we learn the feature representations on an unlabelled sequence of tasks and show that reliance on annotated data is not necessary for continual learning. We conduct a systematic study analyzing the learned feature representations and show that unsupervised visual representations are surprisingly more robust to catastrophic forgetting, consistently achieve better performance, and generalize better to out-of-distribution tasks than SCL. Furthermore, we find that UCL achieves a smoother loss landscape through qualitative analysis of the learned representations and learns meaningful feature representations. Additionally, we propose Lifelong Unsupervised Mixup (LUMP), a simple yet effective technique that interpolates between the current task and previous tasks' instances to alleviate catastrophic forgetting for unsupervised representations.","url_abs":"https://arxiv.org/abs/2110.06976v3","url_pdf":"https://arxiv.org/pdf/2110.06976v3.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":"rethinking-the-representational-continuity-1","repo_url":"https://github.com/divyam3897/ucl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"}],"methods":[{"method_slug":"mixup","method_name":"Mixup"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.06976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06976"}},"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/divyam3897/ucl","reach":null}],"summary":{"ran":3,"ran_draft_wrong":1,"unverified":2},"by_repo_kind":{"official":{"samples":6,"ran":4,"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":"60c8f08559dd5eba","entry":"Buffer","repo":"divyam3897/ucl","repo_kind":"official","path":"models/mixup.py","file_url":"https://github.com/divyam3897/ucl/blob/HEAD/models/mixup.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"60c8f08559dd5eba"}},{"code_sha256_prefix":"cbc529c692526afb","entry":"LARS","repo":"divyam3897/ucl","repo_kind":"official","path":"models/mixup.py","file_url":"https://github.com/divyam3897/ucl/blob/HEAD/models/mixup.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"cbc529c692526afb"}},{"code_sha256_prefix":"812d838b69cc5520","entry":"LR_Scheduler","repo":"divyam3897/ucl","repo_kind":"official","path":"models/mixup.py","file_url":"https://github.com/divyam3897/ucl/blob/HEAD/models/mixup.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"812d838b69cc5520"}},{"code_sha256_prefix":"c3afd56f1b041572","entry":"get_optimizer","repo":"divyam3897/ucl","repo_kind":"official","path":"models/mixup.py","file_url":"https://github.com/divyam3897/ucl/blob/HEAD/models/mixup.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"c3afd56f1b041572"}},{"code_sha256_prefix":"903bc2472f853661","entry":"ContinualModel","repo":"divyam3897/ucl","repo_kind":"official","path":"models/mixup.py","file_url":"https://github.com/divyam3897/ucl/blob/HEAD/models/mixup.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":"903bc2472f853661"}},{"code_sha256_prefix":"8c6f91057e597c0a","entry":"Mixup","repo":"divyam3897/ucl","repo_kind":"official","path":"models/mixup.py","file_url":"https://github.com/divyam3897/ucl/blob/HEAD/models/mixup.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":"8c6f91057e597c0a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}