{"url":"/method/moga-a","slug":"moga-a","name":"MoGA-A","full_name":"MoGA-A","full_name_withheld":false,"description_markdown":"**MoGA-A** is a convolutional neural network optimized for mobile latency and discovered via Mobile GPU-Aware (MoGA) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). The basic building block is MBConvs (inverted residual blocks) from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2). Squeeze-and-excitation layers are also experimented with.","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/1908.01314v4","title":"MoGA: Searching Beyond MobileNetV3","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/xiaomi-automl/MoGA/blob/5ddc4bb9a1be845953947f6ec056cfce5ef965b9/models/MoGA_A.py#L122","code_snippet_url_on_a_code_host":true,"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/moga-searching-beyond-mobilenetv3","title":"MoGA: Searching Beyond MobileNetV3","date":"2019-08-04","arxiv_id":"1908.01314","n_code_links":2,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/automl","name":"AutoML","papers":1},{"task":null,"name":"CPU","papers":1},{"task":null,"name":"GPU","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/architecture-search","name":"Neural Architecture Search","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2019","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/moga-a"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}