Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization [ECMLPKDD 2019] [Code] (AFLAC) [84] Feature Alignment and Restoration for Domain Generalization and Adaptation [arXiv 2020] (FAR) [189] Representation via Representations: Domain Generalization via Adversarially Learne...
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation [CVPR2021] [Pytorch] Self-adaptive Re-weighted Adversarial Domain Adaptation [IJCAI2020] DIRL: Domain-Invariant Reperesentation Learning Approach for Sim-to-Real Transfer [CoRL2020] [Project] Classes Matter: A ...
Contribution: MFT further encourages the language model to learn domain-invariant representations by jointly optimizing a series of novel domain corruption loss functions. Approach: In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), serving as a meta-learner to s...
Incremental adversarial domain adaptation (IADA) [63] adapts to continually changing domains by adversarially aligning source and target features. [59] aims to continually adapt the unseen visual domain while alleviate the forget- ting on the seen domain without retaining the source train- ing data....
Semantics disentangling for generalized zero-shot learning. In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. [6] Xinjie Fan, Qifei Wang, Junjie Ke, Feng Yang, Boqing Gong, and Mingyuan Zhou. Adversarially adaptive normal- ization for single domain ...
[58] incorporates the concept of relevance between the source and target domains via meta-learning. Similarly, Meta [68] leverages domain-invariant attributes through normalization statistics, while Meta-DMoE [76] strives to extract knowledge from an aggregation of multiple experts. However, these ...
Metric learning-based methods (UML)Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias Fang, Chen, Ye Xu, and Daniel N. Rockmore. Proceedings of the IEEE International Conference on Computer Vision(ICCV) 2013. ...
(UML)Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias Fang, Chen, Ye Xu, and Daniel N. Rockmore. Proceedings of the IEEE International Conference on Computer Vision(ICCV) 2013. Support vector machine (SVM)-based methods ...
MetaReg: Towards Domain Generalization using Meta-Regularization[NIPS2018] Deep Domain Generalization via Conditional Invariant Adversarial Networks[ECCV2018] Domain Generalization with Adversarial Feature Learning[CVPR2018] Journal Correlation-aware Adversarial Domain Adaptation and Generalization[Pattern Recognition...
MetaReg: Towards Domain Generalization using Meta-Regularization [NIPS2018] Deep Domain Generalization via Conditional Invariant Adversarial Networks [ECCV2018] Domain Generalization with Adversarial Feature Learning [CVPR2018] Domain Randomization Conference DeceptionNet: Network-Driven Domain Randomization [ICCV...