{"url":"/method/greedynas-a","slug":"greedynas-a","name":"GreedyNAS-A","full_name":"GreedyNAS-A","full_name_withheld":false,"description_markdown":"**GreedyNAS-A** is a convolutional neural network discovered using the [GreedyNAS](https://paperswithcode.com/method/greedynas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building blocks used are inverted residual blocks (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)) and squeeze-and-excitation blocks.","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/2003.11236v1","title":"GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Convolutional Neural Networks","url":"/methods/category/convolutional-neural-networks","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/greedynas-towards-fast-one-shot-nas-with","title":"GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet","date":"2020-03-25","arxiv_id":"2003.11236","n_code_links":0,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/all","name":"All","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/architecture-search","name":"Neural Architecture Search","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2020","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/greedynas-a"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}