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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 8: AAAI-21 Technical Tracks 8

DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation

February 1, 2023

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Authors

Haoyue Bai

The Hong Kong University of Science and Technology


Rui Sun

Huawei Noah's Ark Lab


Lanqing Hong

Huawei Noah's Ark Lab


Fengwei Zhou

Huawei Noah's Ark Lab


Nanyang Ye

Shanghai Jiao Tong University


Han-Jia Ye

Nanjing University


S.-H. Gary Chan

The Hong Kong University of Science and Technology


Zhenguo Li

Huawei Noah's Ark Lab


DOI:

10.1609/aaai.v35i8.16829


Abstract:

While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework for a wide range of applications is challenging, mainly due to different kinds of distribution shifts in the real world, such as the shift across domains or the extrapolation of correlation. Most of the previous approaches can only solve one specific distribution shift, leading to unsatisfactory performance when applied to various OoD benchmarks. In this work, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization. Specifically, DecAug disentangles the category-related and context-related features by orthogonalizing the two gradients (w.r.t. intermediate features) of losses for predicting category and context labels, where category-related features contain causal information of the target object, while context-related features cause distribution shifts between training and test data. Furthermore, we perform gradient-based augmentation on context-related features to improve the robustness of learned representations. Experimental results show that DecAug outperforms other state-of-the-art methods on various OoD datasets, which is among the very few methods that can deal with different types of OoD generalization challenges.

Topics: AAAI

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HOW TO CITE:

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation Proceedings of the AAAI Conference on Artificial Intelligence (2021) 6705-6713.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation AAAI 2021, 6705-6713.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li (2021). DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 6705-6713.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li. DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.6705-6713.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li. 2021. DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 6705-6713.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li. (2021) "DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.6705-6713

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li, "DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation", AAAI, p.6705-6713, 2021.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li. "DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.6705-6713.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li. "DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 6705-6713.

Haoyue Bai||Rui Sun||Lanqing Hong||Fengwei Zhou||Nanyang Ye||Han-Jia Ye||S.-H. Gary Chan||Zhenguo Li. DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation. AAAI[Internet]. 2021[cited 2023]; 6705-6713.


ISSN: 2374-3468


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