{"url":"/method/shape-adaptor","slug":"shape-adaptor","name":"Shape Adaptor","full_name":"Shape Adaptor","full_name_withheld":false,"description_markdown":"**Shape Adaptor** is a novel resizing module for neural networks. It is a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided [convolution](https://paperswithcode.com/method/convolution). This module allows for a learnable shaping factor which differs from the traditional resizing layers that are fixed and deterministic.\r\n\r\nImage Source: [Liu et al.](https://arxiv.org/pdf/2008.00892v2.pdf)","description_state":"present","introduced_year":null,"introduced_by":{"title":"Shape Adaptor: A Learnable Resizing Module","paper":"/paper/shape-adaptor-a-learnable-resizing-module","first_author":"Shikun Liu","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/shape-adaptor-a-learnable-resizing-module"},"source":{"url":"https://arxiv.org/abs/2008.00892v2","title":"Shape Adaptor: A Learnable Resizing Module","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/lorenmt/shape-adaptor/blob/7dc323d26b8c6fda8d0087023a0a60df0b8d5e91/model_list.py#L9","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Pooling Operations","url":"/methods/category/pooling-operations","pwc_aliases":["pooling-operation"]},{"area":"General","area_id":"general","collection":"AutoML","url":"/methods/category/automl","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/balanced-mixture-of-supernets-for-learning","title":"Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture","date":"2023-06-21","arxiv_id":"2306.11982","n_code_links":1,"syntology":null},{"paper":"/paper/shape-adaptor-a-learnable-resizing-module","title":"Shape Adaptor: A Learnable Resizing Module","date":"2020-08-03","arxiv_id":"2008.00892","n_code_links":1,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":2},{"task":"/task/architecture-search","name":"Neural Architecture Search","papers":2},{"task":"/task/automl","name":"AutoML","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1},{"task":"/task/image-classification","name":"image-classification","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2020","papers":1},{"year":"2023","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/shape-adaptor"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}