Learning to Optimize Domain Specific Normalization for Domain Generalization

2020

Citations in the corpus

• SWAD: Domain Generalization by Seeking Flat Minima (2021, neutral): “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.”
• Generalizing to Unseen Domains: A Survey on Domain Generalization (2021, unknown): “…Explicit feature alignment: MTAE [67]/ CCSA [68]/ BNE [69]/ SNR [70, 71]/ MMD-AAE [72]/ DDGFD [73]/ DSDGN [74]/ DG-YOLO [75]/ MatchDG [76]/ DSON [77]/ DFDG [78]/ DSAF [79]/ AdaRNN [80]/ ASR-Norm [81]/ RNA-Net [82]/ FAR [83]/ LAG [84]/ [85, 86, 87, 88, 89, 90] Domain adversarial learning: DANN…”
• RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening (2021, background): “Recently, several studies [44, 55] have shown the effectiveness of exploiting both batch normalization (BN) [24] and instance normalization (IN) [59] within DNNs to solve the DG problem.”
• Domain Generalization with MixStyle (2021, background): “Some works explored domain-specific parameterization, such as domain-specific weight matrices (Li et al., 2017) and domain-specific BN (Seo et al., 2020).”
• Normalization Techniques in Training DNNs: Methodology, Analysis and Application (2020, unknown): “Besides population statistics, Seo et al . also exploited the affine transform (Eqn.22) of BN to represent the domain-specific information in their proposed domain-specific optimized normalization (DSON) [236].”
• Deep Stable Learning for Out-Of-Distribution Generalization (2021, background): “A common approach is to extract domain-invariant features over multiple source domains [18, 29, 37, 39, 47, 11, 25, 50, 56, 47] or to aggregate domain-specific modules [43, 44].”