{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/high-capacity-expert-binary-networks-1","title":"High-Capacity Expert Binary Networks","arxiv_id":"2010.03558","date":"2020-10-07","proceeding":"ICLR 2021 1","authors":["Adrian Bulat","Brais Martinez","Georgios Tzimiropoulos"],"abstract":"Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between binary models and their real-valued counterparts remains an unsolved challenging research problem. To this end, we make the following 3 contributions: (a) To increase model capacity, we propose Expert Binary Convolution, which, for the first time, tailors conditional computing to binary networks by learning to select one data-specific expert binary filter at a time conditioned on input features. (b) To increase representation capacity, we propose to address the inherent information bottleneck in binary networks by introducing an efficient width expansion mechanism which keeps the binary operations within the same budget. (c) To improve network design, we propose a principled binary network growth mechanism that unveils a set of network topologies of favorable properties. Overall, our method improves upon prior work, with no increase in computational cost, by $\\sim6 \\%$, reaching a groundbreaking $\\sim 71\\%$ on ImageNet classification. Code will be made available $\\href{https://www.adrianbulat.com/binary-networks}{here}$.","url_abs":"https://arxiv.org/abs/2010.03558v2","url_pdf":"https://arxiv.org/pdf/2010.03558v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"high-capacity-expert-binary-networks-1","repo_url":"https://github.com/1adrianb/expert-binary-networks","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"binarization","task_name":"Binarization"},{"task_slug":"classification-with-binary-neural-network","task_name":"Classification with Binary Neural Network"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.03558","atlas_url":"https://app.syntology.ai/?focus=2010.03558","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.03558"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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