Heterogeneous Domain Generalization Via Domain Mixup
By Yufei Wang, Haoliang Li, Alex ChiChung Kot. 2020
173 citations recorded by the source corpus.
One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain generalization which aims to improve the generalization capability across different tasks, which is, how to learn a DCNN model with multiple domain data such that the trained feature extractor can be generalized to supporting recognition of novel categories in a novel target domain. To solve this problem, we propose a novel heterogeneous domain generalization method by mixing up samples across multiple source domains with two different sampling strategies. Our experimental results based on the Visual Decathlon benchmark demonstrates the effectiveness of our proposed method.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
- Chen Change Loy
- Yu Qiao
- Luc Van Gool
- Ziwei Liu
- Tao A. Xiang
- Xiaogang Wang
- Dacheng Tao
- Ming-Hsuan Yang
Citations in the corpus
- SelfReg: Self-supervised Contrastive Regularization for Domain Generalization (2021, methodology): “[43] as defined as follows: ūi = γu c i + (1− γ)uj∈[1,N ] (2)”
- Domain Generalization: A Survey (2021, background): “In [165], Mixup is applied to samples from different source domains.”
- Domain Generalization using Causal Matching (2020, methodology): “(Carlucci et al., 2019), S-MLDG (Li et al., 2020), D-SAM (D’Innocente & Caputo, 2018), MMLD (Matsuura & Harada, 2020), DDAIG (Zhou et al., 2020) SagNet (Nam et al., 2019), DDEC (Asadi et al., 2019), DANN (Ganin et al., 2016), C-DANN (Li et al., 2018d), DRO (Sagawa et al., 2019), Mixup (Xu et al., 20”
- Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (2021, methodology): “These results include the following algorithms: Empirical Risk Minimization (ERM), Group Distributionally Robust Optimization (GroupDRO) [41], Inter-domain Mixup (Mixup) [56, 58, 53], Meta-Learning for Domain Generalization (MLDG) [28], DomainAdversarial Neural Networks (DANN) [15], Class-conditiona”