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Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains. Although a variety of DG methods have been proposed, a recent study shows that under a fair evaluation protocol, called DomainBed, the simple empirical risk minimization (ERM) approach works comparable to or even outperforms previous methods. Unfortunately, simply solving ERM on a complex, non-convex loss function can easily lead to sub-optimal generalizability by seeking sharp minima. In this paper, we theoretically show that finding flat minima results in a smaller domain generalization gap. We also propose a simple yet effective method, named Stochastic Weight Averaging Densely (SWAD), to find flat minima. SWAD finds flatter minima and suffers less from overfitting than does the vanilla SWA by a dense and overfit-aware stochastic weight sampling strategy. SWAD shows state-of-the-art performances on five DG benchmarks, namely PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet, with consistent and large margins of +1.6% averagely on out-of-domain accuracy. We also compare SWAD with conventional generalization methods, such as data augmentation and consistency regularization methods, to verify that the remarkable performance improvements are originated from by seeking flat minima, not from better in-domain generalizability. Last but not least, SWAD is readily adaptable to existing DG methods without modification; the combination of SWAD and an existing DG method further improves DG performances. Source code is available at https://github.com/khanrc/swad.
Author Information
Junbum Cha (Kakao Brain)
AI Researcher @ Kakao Brain
Sanghyuk Chun (NAVER AI Lab)
I'm a research scientist and tech leader at NAVER AI Lab, working on machine learning and its applications. In particular, my research interests focus on bridging the gap between two gigantic topics: reliable machine learning tasks (e.g., robustness [C3, C9, C10, W1, W3], de-biasing or domain generalization [C6, A6], uncertainty estimation [C11, A3], explainability [C5, C11, A2, A4, W2], and fair evaluation [C5, C11]) and learning with limited annotations (e.g., multi-modal learning [C11], weakly-supervised learning [C2, C3, C4, C5, C7, C8, C12, W2, W4, W5, W6, A2, A4], and self-supervised learning). I have contributed large-scale machine learning algorithms [C3, C9, C10, C13] in NAVER AI Lab as well. Prior to working at NAVER, I worked as a research engineer at the advanced recommendation team (ART) in Kakao from 2016 to 2018. I received a master's degree in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST) in 2016. During my master's degree, I researched a scalable algorithm for robust subspace clustering (the algorithm is based on robust PCA and k-means clustering). Before my master's study, I worked at IUM-SOCIUS in 2012 as a software engineering internship. I also did a research internship at Networked and Distributed Computing System Lab in KAIST and NAVER Labs during summer 2013 and fall 2015, respectively.
Kyungjae Lee (Seoul National University)
Han-Cheol Cho (NAVER)
Studied natural language processing in graduate school. Now working on DL for computer vision.
Seunghyun Park (Clova AI Research, Naver Corp.)
Yunsung Lee (Korea University)
Sungrae Park (UPSTAGE)
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