{"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/pc-darts-partial-channel-connections-for","title":"PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search","arxiv_id":"1907.05737","date":"2019-07-12","proceeding":"ICLR 2020 1","authors":["Yuhui Xu","Lingxi Xie","Xiaopeng Zhang","Xin Chen","Guo-Jun Qi","Qi Tian","Hongkai Xiong"],"abstract":"Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture. In this paper, we present a novel approach, namely, Partially-Connected DARTS, by sampling a small part of super-network to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut. This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels. We alleviate it using edge normalization, which adds a new set of edge-level parameters to reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoys both faster speed and higher training stability. Experimental results demonstrate the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.57% on CIFAR10 with merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) using 3.8 GPU-days for search. Our code has been made available at: https://github.com/yuhuixu1993/PC-DARTS.","url_abs":"https://arxiv.org/abs/1907.05737v4","url_pdf":"https://arxiv.org/pdf/1907.05737v4.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":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/yuhuixu1993/PC-DARTS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/aragakiyuiii/gumbel-pdarts-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/chenxin061/pdarts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/ddghost/new_darts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/peteryuX/pcdarts-tf2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/xkp793003821/PC-DARTS-COOPER","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/MS-Mind/MS-Code-06/tree/main/PDarts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"pc-darts-partial-channel-connections-for","repo_url":"https://github.com/Mind23-2/MindCode-5/tree/main/PDarts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[{"method_slug":"darts","method_name":"DARTS"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/neural-architecture-search-on-cifar-10","task":"Neural Architecture Search","dataset":"CIFAR-10","model":"PC-DARTS-CIFAR","rank_in_archive_order":20,"of":41,"metrics":{"Top-1 Error Rate":"2.51%"},"uses_additional_data":false},{"leaderboard":"/sota/neural-architecture-search-on-cifar-10","task":"Neural Architecture Search","dataset":"CIFAR-10","model":"PC-DARTS","rank_in_archive_order":27,"of":41,"metrics":{"Parameters":"3.6M","Search Time (GPU days)":"0.1","Top-1 Error Rate":"2.57%"},"uses_additional_data":false},{"leaderboard":"/sota/neural-architecture-search-on-imagenet","task":"Neural Architecture Search","dataset":"ImageNet","model":"PC-DARTS (ImageNet)","rank_in_archive_order":102,"of":135,"metrics":{"Accuracy":"75.8","MACs":"597M","Params":"5.3M","Top-1 Error Rate":"24.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1907.05737","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05737"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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