{"type":"ScholarlyArticle","title":"GeNAS: Neural Architecture Search with Better Generalization","authors":[{"name":"Joonhyun Jeong","slug":"joonhyun-jeong","given":"Joonhyun","family":"Jeong","affiliation":"KAIST"},{"name":"Joonsang Yu","slug":"joonsang-yu","given":"Joonsang","family":"Yu","affiliation":"NAVER AI Lab"},{"name":"Geondo Park","slug":"geondo-park","given":"Geondo","family":"Park","affiliation":"KAIST"},{"name":"Dongyoon Han","slug":"dongyoon-han","given":"Dongyoon","family":"Han","affiliation":"NAVER AI Lab"},{"name":"YoungJoon Yoo","slug":"youngjoon-yoo","given":"YoungJoon","family":"Yoo","affiliation":""}],"abstract":"Neural Architecture Search (NAS) aims to automatically excavate the optimal network architecture with superior test performance. Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization. We demonstrate that the flatness of the loss surface can be a promising proxy for predicting the generalization capability of neural network architectures. We evaluate our proposed method on various search spaces, showing similar or even better performance compared to the state-of-the-art NAS methods. Notably, the resultant architecture found by flatness measure generalizes robustly to various shifts in data distribution (e.g. ImageNet-V2,-A,-O), as well as various tasks such as object detection and semantic segmentation.","doi":"10.24963/ijcai.2023/101","url":"https://ijcai.org/proceedings/2023/101","pdf":"https://ijcai.org/proceedings/2023/0101.pdf","markdown":"https://ijcai.org/proceedings/2023/101.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/101","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":101,"pages":{"first":911,"last":919},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Machine learning for vision"],"videos":[],"references":[{"position":1,"raw":"[Baker et al., 2016] Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. 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