{"url":"/method/self-adaptive-training","slug":"self-adaptive-training","name":"Self-adaptive Training","full_name":"Self-adaptive Training","full_name_withheld":false,"description_markdown":"**Self-adaptive Training** is a training algorithm that dynamically corrects problematic training labels by model predictions to improve generalization of deep learning for potentially corrupted training data. Accumulated predictions are used to augment the training dynamics. The use of an exponential-moving-average scheme alleviates the instability issue of model predictions, smooths out the training target during the training process and enables the algorithm to completely change the training labels if necessary.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Self-Adaptive Training: beyond Empirical Risk Minimization","paper":"/paper/self-adaptive-training-beyond-empirical-risk","first_author":"Lang Huang","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/self-adaptive-training-beyond-empirical-risk"},"source":{"url":"https://arxiv.org/abs/2002.10319v2","title":"Self-Adaptive Training: beyond Empirical Risk Minimization","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Robust Training","url":"/methods/category/robust-training","pwc_aliases":[]}],"n_papers_tagged":4,"archive_num_papers":4,"papers_newest_first":[{"paper":"/paper/self-improving-safety-performance-of","title":"Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms","date":"2022-10-29","arxiv_id":"2210.16575","n_code_links":2,"syntology":null},{"paper":"/paper/sat-self-adaptive-training-for-fashion","title":"SAT: Self-adaptive training for fashion compatibility prediction","date":"2022-06-25","arxiv_id":"2206.12622","n_code_links":1,"syntology":null},{"paper":"/paper/self-adaptive-training-bridging-the","title":"Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning","date":"2021-01-21","arxiv_id":"2101.08732","n_code_links":2,"syntology":null},{"paper":"/paper/self-adaptive-training-beyond-empirical-risk","title":"Self-Adaptive Training: beyond Empirical Risk Minimization","date":"2020-02-24","arxiv_id":"2002.10319","n_code_links":4,"syntology":{"ran":2,"of":2,"unverified":0,"pointer_only":1}}],"papers_shown":4,"tasks":[{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":1},{"task":"/task/diversity","name":"Diversity","papers":1},{"task":"/task/classification","name":"General Classification","papers":1},{"task":"/task/linear-evaluation","name":"Linear evaluation","papers":1},{"task":"/task/prediction","name":"Prediction","papers":1},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":1},{"task":"/task/representation-learning","name":"Representation Learning","papers":1},{"task":"/task/safe-reinforcement-learning","name":"Safe Reinforcement Learning","papers":1},{"task":"/task/self-learning","name":"Self-Learning","papers":1},{"task":"/task/self-supervised-learning","name":"Self-Supervised Learning","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1},{"task":null,"name":"Triplet","papers":1}],"tasks_shown":12,"n_tasks":12,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1},{"year":"2022","papers":2}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/self-adaptive-training"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}