Domain Generalization via Entropy Regularization

By Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao. 2020

317 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Kaiyang Zhou
2. Yu Qiao
3. Ziwei Liu
4. Chen Change Loy
5. Tao A. Xiang
6. Xiao Wang
7. Xiaogang Wang
8. Luc Van Gool

Citations in the corpus

• SWAD: Domain Generalization by Seeking Flat Minima (2021, background): “MASF [14] 82.7 - - - - - DMG [33] 83.4 - - - 43.6 - MetaReg [15] 83.6 - - - 43.6 - ER [12] 85.3 - - - - - pAdaIN [47] 85.4 - - - - - EISNet [48] 85.8 - - - - - DSON [30] 86.”
• Domain Generalization: A Survey (2021, methodology): “- Domain-Adversarial Learning [28], [39], [59], [62], [145], [154], [155], [156], [157], [158]”
• Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization (2021, methodology): “A number of methods have been proposed for domain discrepancy minimization (Li et al., 2020), adversarial training (Rah-man et al., 2020; Zhao et al., 2020c), invariance representation learning (Zhou et al., 2020; Piratla et al., 2020), etc. Recently, there is significant interest on conducting DG…”
• Domain Generalization using Causal Matching (2020, methodology): “More-over, on average MatchDG , MDGHybrid are better than many previous approaches D-MTAE (Ghifary et al., 2015), DBADG (Li et al., 2017), CIDDG (Li et al., 2018d), HEX (Wang et al., 2019) and FeatureCritic (Li et al., 2019b), but some other methods like MASF (Dou et al., 2019), DGER (Zhao et al., 2”
• Towards Out-Of-Distribution Generalization: A Survey (2021, background): “There are works [6], [44] [46] propose an additional entropy-regularization approach that learns invariant representations via minimizing the KL-divergence between the conditional distribution of different source domains.”