Correlation-aware Adversarial Domain Adaptation and Generalization

@article{Rahman2020CorrelationawareAD,
  title={Correlation-aware Adversarial Domain Adaptation and Generalization},
  author={Mohammad Mahfujur Rahman and Clinton Fookes and Mahsa Baktash and Sridha Sridharan},
  journal={Pattern Recognit.},
  year={2020},
  volume={100},
  pages={107124}
}
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References

SHOWING 1-10 OF 49 REFERENCES
Multi-Component Image Translation for Deep Domain Generalization
TLDR
A novel deep domain generalization architecture utilizing synthetic data generated by a Generative Adversarial Network is proposed, and the discrepancy between the generated images and synthetic images is minimized using existing domain discrepancy metrics such as maximum mean discrepancy or correlation alignment.
Adversarial Discriminative Domain Adaptation
TLDR
It is shown that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and the promise of the approach is demonstrated by exceeding state-of-the-art unsupervised adaptation results on standard domain adaptation tasks as well as a difficult cross-modality object classification task.
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation
TLDR
This work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation and achieves the state-of-the-art performance on unsupervised domain adaptation of digit classification and object recognition.
Domain-Adversarial Training of Neural Networks
TLDR
A new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions, which can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer.
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks
TLDR
This work proposes an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space by inducing a symbiotic relationship between the learned embedding and a generative adversarial network.
Deep Domain Generalization via Conditional Invariant Adversarial Networks
TLDR
This work proposes an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning and proves the effectiveness of the proposed method.
Deep Transfer Learning with Joint Adaptation Networks
TLDR
JAN is presented, which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion.
Conditional Generative Adversarial Network for Structured Domain Adaptation
TLDR
A principled way to conduct structured domain adaption for semantic segmentation by integrating GAN into the FCN framework to mitigate the gap between source and target domains is proposed.
Deep Domain Generalization With Structured Low-Rank Constraint.
  • Zhengming DingYun Fu
  • Computer Science
    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
  • 2018
TLDR
A deep domain generalization framework with structured low-rank constraint to facilitate the unseen target domain evaluation by capturing consistent knowledge across multiple related source domains and the experimental results show the superiority of the algorithm by comparing it with state-of-the-artdomain generalization approaches.
Adaptive Batch Normalization for practical domain adaptation
...
...