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SimpleDG: Simple Domain Generalization Baseline without Bells and Whistles

arXiv 2210.14507 ↗First posted 26 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

We present a simple domain generalization baseline, which wins second place in both the common context generalization track and the hybrid context generalization track respectively in NICO CHALLENGE 2022. We verify the founding in recent literature, domainbed, that ERM is a strong baseline compared to recent state-of-the-art domain generalization methods and propose SimpleDG which includes several simple yet effective designs that further boost generalization performance. Code is available at https://github.com/megvii-research/SimpleDG

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 26 Oct 2022.

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21 of 27 references.

References · 21

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  10. 2020In Search of Lost Domain Generalizationcited by 1,054
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  12. 2016Deep Networks with Stochastic Depthcited by 979
9 more
  1. 2021Going deeper with Image Transformerscited by 764
  2. 2021Domain Generalization with MixStylecited by 566
  3. 2020FDA: Fourier Domain Adaptation for Semantic Segmentationcited by 541
  4. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 434
  5. 2020Self-Challenging Improves Cross-Domain Generalizationcited by 426
  6. 2020Gradient Starvation: A Learning Proclivity in Neural Networkscited by 267
  7. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  8. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  9. 2022RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustnesscited by 27

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