{"url":"/method/autodropout","slug":"autodropout","name":"AutoDropout","full_name":"AutoDropout","full_name_withheld":false,"description_markdown":"**AutoDropout** automates the process of designing [dropout](https://paperswithcode.com/method/dropout) patterns using a [Transformer](https://paperswithcode.com/method/transformer) based controller. In this method, a controller learns to generate a dropout pattern at every channel and layer of a target network, such as a [ConvNet](https://paperswithcode.com/methods/category/convolutional-neural-networks) or a Transformer. The target network is then trained with the dropped-out pattern, and its resulting validation performance is used as a signal for the controller to learn from. The resulting pattern is applied to a convolutional output channel, which is a common building block of image recognition models.\r\n\r\nThe controller network generates the tokens to describe the configurations of the dropout pattern. The tokens are generated like words in a language model. For every layer in a ConvNet, a group of 8 tokens need to be made to create a dropout pattern. These 8 tokens are generated sequentially. In the figure above, size, stride, and repeat indicate the size and the tiling of the pattern; rotate, shear_x, and shear_y specify the geometric transformations of the pattern; share_c is a binary deciding whether a pattern is applied to all $C$ channels; and residual is a binary deciding whether the pattern is applied to the residual branch as well. If we need $L$ dropout patterns, the controller will generate $8L$ decisions.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2101.01761v1","title":"AutoDropout: Learning Dropout Patterns to Regularize Deep Networks","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":"Regularization","url":"/methods/category/regularization","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/autodropout-learning-dropout-patterns-to","title":"AutoDropout: Learning Dropout Patterns to Regularize Deep Networks","date":"2021-01-05","arxiv_id":"2101.01761","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/machine-translation","name":"Machine Translation","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/autodropout"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}