{"url":"/method/fraternal-dropout","slug":"fraternal-dropout","name":"Fraternal Dropout","full_name":"Fraternal Dropout","full_name_withheld":false,"description_markdown":"**Fraternal Dropout** is a regularization method for recurrent neural networks that trains two identical copies of an RNN (that share parameters) with different [dropout](https://paperswithcode.com/method/dropout) masks while minimizing the difference between their (pre-[softmax](https://paperswithcode.com/method/softmax)) predictions. This encourages the representations of RNNs to be invariant to dropout mask, thus being robust.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Fraternal Dropout","paper":"/paper/fraternal-dropout","first_author":"Konrad Zolna","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/fraternal-dropout"},"source":{"url":"http://arxiv.org/abs/1711.00066v4","title":"Fraternal Dropout","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/kondiz/fraternal-dropout","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Regularization","url":"/methods/category/regularization","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"Preventing posterior collapse in variational autoencoders for text generation via decoder regularization","date":"2021-10-28","arxiv_id":"2110.14945","n_code_links":0,"syntology":null},{"paper":"/paper/fraternal-dropout","title":"Fraternal Dropout","date":"2017-10-31","arxiv_id":"1711.00066","n_code_links":1,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/image-captioning","name":"Image Captioning","papers":1},{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/text-generation","name":"Text Generation","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2017","papers":1},{"year":"2021","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/fraternal-dropout"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}