{"doi":"10.1109/access.2023.3339775","title":"Evaluating and Improving Domain Invariance in Contrastive Self-Supervised Learning by Extrapolating the Loss Function","abstract":"Despite the remarkable progress of self-supervised learning (SSL), how self-supervised representations generalize to out-of-distribution data remains little understood. In this paper, we study the effects of distribution shifts on self-supervised representations. Our findings indicate that self-supervised representation learning is more robust than traditional supervised learning (52.8% versus 17.1% on the CMNIST dataset, 63.6% versus 60.6% on the Waterbirds dataset). However, self-supervised representations still suffer significantly from domain shifts, especially when spurious correlations are present. Motivated by this limitation, we propose a risk-extrapolated information NCE (ReinformNCE) to facilitate self-supervised learning algorithms to learn more stable representations. Our approach integrates the infoNCE loss function and a robust optimization approach that extrapolates the risks of training domains. Extensive experiments show that ReinformNCE helps to extract domain-invariant self-supervised representations and it substantially improves the robustness of the self-supervised representations (68.2% versus 52.8% on the CMNIST dataset, 77.9% versus 63.6% on the Waterbirds dataset). To the best of our knowledge, this is the first work demonstrating the feasibility of learning domain-invariant representations based on robust optimization theory and without supervised information.","journal":"IEEE Access","year":2023,"id":382819,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9567,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":639237,"name":"Hien Van Nguyen","orcid":"0000-0001-7280-2182","position":1,"is_corresponding":false},{"id":1149264,"name":"Samira Zare","orcid":"0000-0002-7828-0562","position":0,"is_corresponding":true}],"reference_count":72,"raw_metadata":null,"created_at":"2026-07-19T01:17:20.969491Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}