Correlation-aware Adversarial Domain Adaptation and Generalization

  title={Correlation-aware Adversarial Domain Adaptation and Generalization},
  author={Mohammad Mahfujur Rahman and Clinton Fookes and Mahsa Baktash and Sridha Sridharan},
  journal={Pattern Recognit.},
Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks
This paper proposes an end-to-end novel semantic consistent generative adversarial network (SCGAN) which can achieve source to target domain matching by capturing semantic information at the feature level and producing images for unsupervised domain adaptation from both the source and the target domains.
Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization
A discriminative domain-invariant adversarial network (DDIAN) is proposed that achieves better prediction on unseen target data during training compared to state-of-the-art domain generalization approaches.
Deep Domain Generalization with Feature-norm Network
This paper introduces an end-to-end feature-norm network (FNN) which is robust to negative transfer as it does not need to match the feature distribution among the source domains and introduces a collaborative feature- norm network (CFNN) to further improve the generalization capability of FNN.
More is Better: A Novel Multi-view Framework for Domain Generalization
This paper investigates that overfitting not only causes the inferior generalization ability to unseen target domains but also leads unstable prediction in the test stage, and proposes a novel multiview DG framework that outperforms several state-of-the-art approaches.
Cross‐domain speaker recognition using domain adversarial siamese network with a domain discriminator
A domain adversarial siamese (DAS) network trying to eliminate the domain influence on speech representation is proposed, which is proved to be more valid for scenarios such as unbalanced data amount and unknown domain, achieving relatively 11 % improvements.
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
. This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to alleviate the
Exploring Dropout Discriminator for Domain Adaptation


Multi-Component Image Translation for Deep Domain Generalization
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
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
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
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
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
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
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
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
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