{"url":"/method/fast-autoaugment","slug":"fast-autoaugment","name":"Fast AutoAugment","full_name":"Fast AutoAugment","full_name_withheld":false,"description_markdown":"**Fast AutoAugment** is an image data augmentation algorithm that finds effective augmentation policies via a search strategy based on density matching, motivated by Bayesian DA. The strategy is to improve the generalization performance of a given network by learning the augmentation policies which treat augmented data as missing data points of training data. However, different from Bayesian DA, the proposed method recovers those missing data points by the exploitation-and-exploration of a family of inference-time augmentations via Bayesian optimization in the policy search phase. This is realized by using an efficient density matching algorithm that does not require any back-propagation for network training for each policy evaluation.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Fast AutoAugment","paper":"/paper/fast-autoaugment","first_author":"Sungbin Lim","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/fast-autoaugment"},"source":{"url":"https://arxiv.org/abs/1905.00397v2","title":"Fast AutoAugment","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/kakaobrain/fast-autoaugment","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Data Augmentation","url":"/methods/category/image-data-augmentation","pwc_aliases":[]}],"n_papers_tagged":7,"archive_num_papers":7,"papers_newest_first":[{"paper":null,"title":"Data Augmentation For Small Object using Fast AutoAugment","date":"2025-06-10","arxiv_id":"2506.08956","n_code_links":0,"syntology":null},{"paper":"/paper/deep-autoaugment-1","title":"Deep AutoAugment","date":"2022-03-11","arxiv_id":"2203.06172","n_code_links":1,"syntology":null},{"paper":"/paper/autoclint-the-winning-method-in-autocv","title":"AutoCLINT: The Winning Method in AutoCV Challenge 2019","date":"2020-05-09","arxiv_id":"2005.04373","n_code_links":1,"syntology":{"ran":3,"of":3,"unverified":0,"pointer_only":0}},{"paper":"/paper/uniformaugment-a-search-free-probabilistic","title":"UniformAugment: A Search-free Probabilistic Data Augmentation Approach","date":"2020-03-31","arxiv_id":"2003.14348","n_code_links":1,"syntology":null},{"paper":"/paper/dada-differentiable-automatic-data","title":"DADA: Differentiable Automatic Data Augmentation","date":"2020-03-08","arxiv_id":"2003.03780","n_code_links":1,"syntology":{"ran":5,"of":9,"unverified":4,"pointer_only":0}},{"paper":"/paper/efficient-model-for-image-classification-with","title":"Efficient Model for Image Classification With Regularization Tricks","date":"2020-02-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/fast-autoaugment","title":"Fast AutoAugment","date":"2019-05-01","arxiv_id":"1905.00397","n_code_links":11,"syntology":{"ran":22,"of":40,"unverified":18,"pointer_only":3}}],"papers_shown":7,"tasks":[{"task":"/task/data-augmentation","name":"Data Augmentation","papers":7},{"task":"/task/image-classification","name":"Image Classification","papers":4},{"task":null,"name":"GPU","papers":2},{"task":"/task/image-classification","name":"image-classification","papers":2},{"task":"/task/automl","name":"AutoML","papers":1},{"task":"/task/machine-learning","name":"BIG-bench Machine Learning","papers":1},{"task":"/task/classification-1","name":"Classification","papers":1},{"task":"/task/classification","name":"General Classification","papers":1},{"task":"/task/image-augmentation","name":"Image Augmentation","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/reinforcement-learning","name":"Reinforcement Learning","papers":1},{"task":"/task/model","name":"model","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":14,"n_tasks":14,"usage_by_year":[{"year":"2019","papers":1},{"year":"2020","papers":4},{"year":"2022","papers":1},{"year":"2025","papers":1}],"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/fast-autoaugment"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}