{"url":"/method/fixmatch","slug":"fixmatch","name":"FixMatch","full_name":"FixMatch","full_name_withheld":false,"description_markdown":"FixMatch is an algorithm that first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image.\r\n\r\nDescription from: [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://paperswithcode.com/paper/fixmatch-simplifying-semi-supervised-learning)\r\n\r\nImage credit:  [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://paperswithcode.com/paper/fixmatch-simplifying-semi-supervised-learning)","description_state":"present","introduced_year":null,"introduced_by":{"title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","paper":"/paper/fixmatch-simplifying-semi-supervised-learning","first_author":"Kihyuk Sohn","n_authors":9,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/fixmatch-simplifying-semi-supervised-learning"},"source":{"url":"https://arxiv.org/abs/2001.07685v2","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","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":"Semi-Supervised Learning Methods","url":"/methods/category/semi-supervised-learning-methods","pwc_aliases":[]}],"n_papers_tagged":85,"archive_num_papers":85,"papers_newest_first":[{"paper":"/paper/anomalymatch-discovering-rare-objects-of","title":"AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning","date":"2025-05-06","arxiv_id":"2505.03509","n_code_links":1,"syntology":null},{"paper":null,"title":"Towards Micro-Action Recognition with Limited Annotations: An Asynchronous Pseudo Labeling and Training Approach","date":"2025-04-10","arxiv_id":"2504.07785","n_code_links":0,"syntology":null},{"paper":null,"title":"Feature Modulation for Semi-Supervised Domain Generalization without Domain Labels","date":"2025-03-26","arxiv_id":"2503.20897","n_code_links":0,"syntology":null},{"paper":null,"title":"Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning","date":"2025-03-13","arxiv_id":"2503.09974","n_code_links":0,"syntology":null},{"paper":null,"title":"Semi-Supervised Learning for Dose Prediction in Targeted Radionuclide: A Synthetic Data Study","date":"2025-03-07","arxiv_id":"2503.05367","n_code_links":0,"syntology":null},{"paper":null,"title":"Enhancing Deep Learning Model Robustness through Metamorphic Re-Training","date":"2024-12-02","arxiv_id":"2412.01958","n_code_links":0,"syntology":null},{"paper":"/paper/rankup-boosting-semi-supervised-regression","title":"RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier","date":"2024-10-29","arxiv_id":"2410.22124","n_code_links":1,"syntology":{"ran":4,"of":9,"unverified":5,"pointer_only":0}},{"paper":null,"title":"SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation","date":"2024-10-17","arxiv_id":"2410.13486","n_code_links":0,"syntology":null},{"paper":null,"title":"Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning","date":"2024-10-15","arxiv_id":"2410.11206","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02192","title":"Unsupervised Domain Adaption Harnessing Vision-Language Pre-training","date":"2024-08-05","arxiv_id":"2408.02192","n_code_links":1,"syntology":{"ran":6,"of":13,"unverified":7,"pointer_only":0}},{"paper":null,"title":"Smooth Pseudo-Labeling","date":"2024-05-23","arxiv_id":"2405.14313","n_code_links":0,"syntology":null},{"paper":"/paper/diffmatch-visual-language-guidance-makes","title":"SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector","date":"2024-05-08","arxiv_id":"2405.04788","n_code_links":2,"syntology":null},{"paper":"/paper/unsupervised-domain-adaption-harnessing","title":"Unsupervised Domain Adaption Harnessing Vision-Language Pre-training","date":"2024-04-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"title":"A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification","date":"2024-03-27","arxiv_id":"2403.18407","n_code_links":0,"syntology":null},{"paper":"/paper/semi-supervised-medical-image-segmentation-6","title":"Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation Strategies","date":"2024-02-17","arxiv_id":"2402.11273","n_code_links":1,"syntology":null},{"paper":null,"title":"Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells","date":"2024-01-14","arxiv_id":"2401.07278","n_code_links":0,"syntology":null},{"paper":"/paper/uncertainty-aware-sampling-for-long-tailed","title":"Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning","date":"2024-01-09","arxiv_id":"2401.04435","n_code_links":1,"syntology":null},{"paper":null,"title":"Debiased Learning for Remote Sensing Data","date":"2023-12-24","arxiv_id":"2312.15393","n_code_links":0,"syntology":null},{"paper":null,"title":"Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization","date":"2023-12-14","arxiv_id":"2312.08631","n_code_links":0,"syntology":null},{"paper":"/paper/generating-unbiased-pseudo-labels-via-a","title":"Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression","date":"2023-11-03","arxiv_id":"2311.01782","n_code_links":1,"syntology":null},{"paper":null,"title":"SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes","date":"2023-11-03","arxiv_id":"2311.01646","n_code_links":0,"syntology":null},{"paper":null,"title":"On Training Implicit Meta-Learning With Applications to Inductive Weighing in Consistency Regularization","date":"2023-10-28","arxiv_id":"2310.18741","n_code_links":0,"syntology":null},{"paper":null,"title":"KD-FixMatch: Knowledge Distillation Siamese Neural Networks","date":"2023-09-11","arxiv_id":"2309.05826","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-sample-utilization-through-sample","title":"Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning","date":"2023-09-07","arxiv_id":"2309.03598","n_code_links":1,"syntology":{"ran":4,"of":5,"unverified":1,"pointer_only":5}},{"paper":null,"title":"Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size","date":"2023-09-07","arxiv_id":"2309.03469","n_code_links":0,"syntology":null},{"paper":null,"title":"Boosting Semi-Supervised Learning by bridging high and low-confidence predictions","date":"2023-08-15","arxiv_id":"2308.07509","n_code_links":0,"syntology":null},{"paper":"/paper/segmatch-a-semi-supervised-learning-method","title":"SegMatch: A semi-supervised learning method for surgical instrument segmentation","date":"2023-08-09","arxiv_id":"2308.05232","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-the-boundaries-of-semi-supervised","title":"Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data","date":"2023-06-02","arxiv_id":"2306.01229","n_code_links":1,"syntology":null},{"paper":"/paper/relationmatch-matching-in-batch-relationships","title":"RelationMatch: Matching In-batch Relationships for Semi-supervised Learning","date":"2023-05-17","arxiv_id":"2305.10397","n_code_links":1,"syntology":{"ran":1,"of":1,"unverified":0,"pointer_only":0}},{"paper":"/paper/distilling-from-similar-tasks-for-transfer","title":"Distilling from Similar Tasks for Transfer Learning on a Budget","date":"2023-04-24","arxiv_id":"2304.12314","n_code_links":1,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/pseudo-label","name":"Pseudo Label","papers":20},{"task":"/task/semi-supervised-image-classification","name":"Semi-Supervised Image Classification","papers":16},{"task":"/task/image-classification","name":"Image Classification","papers":8},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":8},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":7},{"task":"/task/representation-learning","name":"Representation Learning","papers":6},{"task":"/task/segmentation","name":"Segmentation","papers":6},{"task":"/task/image-classification","name":"image-classification","papers":6},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":5},{"task":"/task/semi-supervised-semantic-segmentation","name":"Semi-Supervised Semantic Segmentation","papers":5},{"task":"/task/active-learning","name":"Active Learning","papers":4},{"task":"/task/classification-1","name":"Classification","papers":4},{"task":"/task/domain-adaptation","name":"Domain Adaptation","papers":4},{"task":"/task/medical-image-segmentation","name":"Medical Image Segmentation","papers":4},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":4},{"task":"/task/domain-generalization","name":"Domain Generalization","papers":3},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":3},{"task":"/task/semi-supervised-domain-generalization","name":"Semi-Supervised Domain Generalization","papers":3},{"task":"/task/semi-supervised-medical-image-segmentation","name":"Semi-supervised Medical Image Segmentation","papers":3},{"task":"/task/unsupervised-domain-adaptation","name":"Unsupervised Domain Adaptation","papers":3}],"tasks_shown":20,"n_tasks":85,"usage_by_year":[{"year":"2020","papers":13},{"year":"2021","papers":19},{"year":"2022","papers":19},{"year":"2023","papers":17},{"year":"2024","papers":12},{"year":"2025","papers":5}],"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/fixmatch"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}