{"type":"ScholarlyArticle","title":"Channel Pruning via Automatic Structure Search","authors":[{"name":"Mingbao Lin","slug":"mingbao-lin","given":"Mingbao","family":"Lin","affiliation":"Media Analytics and Computing Laboratory , Department of Artificial Intelligence , School of Informatics , Xiamen University , China"},{"name":"Rongrong Ji","slug":"rongrong-ji","given":"Rongrong","family":"Ji","affiliation":"Media Analytics and Computing Laboratory , Department of Artificial Intelligence , School of Informatics , Xiamen University , China"},{"name":"Yuxin Zhang","slug":"yuxin-zhang","given":"Yuxin","family":"Zhang","affiliation":"Media Analytics and Computing Laboratory , Department of Artificial Intelligence , School of Informatics , Xiamen University , China"},{"name":"Baochang Zhang","slug":"baochang-zhang","given":"Baochang","family":"Zhang","affiliation":"School of Automation Science and Electrical Engineering , Beihang University , China"},{"name":"Yongjian Wu","slug":"yongjian-wu","given":"Yongjian","family":"Wu","affiliation":"Tencent Youtu Lab , Tencent Technology (Shanghai) Co. , Ltd , China"},{"name":"Yonghong Tian","slug":"yonghong-tian","given":"Yonghong","family":"Tian","affiliation":"School of Electronics Engineering and Computer Science , Peking University , Beijing , China"}],"abstract":"Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting \"important\" channels as previous works did. To solve the intractably huge combinations of pruned structure for deep networks, we first propose to shrink the combinations where the preserved channels are limited to a specific space, thus the combinations of pruned structure can be significantly reduced. And then, we formulate the search of optimal pruned structure as an optimization problem and integrate the ABC algorithm to solve it in an automatic manner to lessen human interference. ABCPruner has been demonstrated to be more effective, which also enables the fine-tuning to be conducted efficiently in an end-to-end manner. The source codes can be available at https: //github.com/lmbxmu/ABCPruner.","doi":"10.24963/ijcai.2020/94","url":"https://ijcai.org/proceedings/2020/94","pdf":"https://ijcai.org/proceedings/2020/0094.pdf","markdown":"https://ijcai.org/proceedings/2020/94.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/94","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Computer Vision","track":"Main track","number":94,"pages":{"first":673,"last":679},"keywords":["Machine Learning: Deep Learning: Convolutional networks","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24061/file_24061.m3u8","length_sec":297,"provider":"hls"}],"references":[{"position":1,"raw":"[Aghasi et al., 2017] Alireza Aghasi, Afshin Abdi, Nam Nguyen, and Justin Romberg. 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