Generalized Domain Conditioned Adaptation Network

@article{Li2021GeneralizedDC,
  title={Generalized Domain Conditioned Adaptation Network},
  author={Shuang Li and Binhui Xie and Qiuxia Lin and Chi Harold Liu and Gao Huang and Guoren Wang},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2021},
  volume={PP}
}
  • S. Li, Binhui Xie, +3 authors Guoren Wang
  • Published 2021
  • Computer Science, Medicine
  • IEEE transactions on pattern analysis and machine intelligence
Domain Adaptation (DA) attempts to transfer knowledge in labeled source domain to unlabeled target domain without requiring target supervision. Recent advanced methods conduct DA mainly by aligning domain distributions. However, the performances of these methods suffer extremely when source and target domains encounter a large domain discrepancy. We argue this limitation may attribute to insufficient domain-specialized feature exploring, because most works merely concentrate on domain-general… Expand
1 Citations
MRSSC: A BENCHMARK DATASET FOR MULTIMODAL REMOTE SENSING SCENE CLASSIFICATION
  • Kang Liu, Aodi Wu, Xue Wan, Shengyang Li
  • 2021
Scene classification based on multi-source remote sensing image is important for image interpretation, and has many applications, such as change detection, visual navigation and image retrieval. DeepExpand

References

SHOWING 1-10 OF 79 REFERENCES
Domain Conditioned Adaptation Network
TLDR
This paper relaxation a shared-convnets assumption made by previous DA methods and proposes a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism, which outperforms existing methods by a large margin. Expand
Deep Residual Correction Network for Partial Domain Adaptation
  • S. Li, C. Liu, +4 authors Zhengming Ding
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2021
TLDR
An efficiently-implemented Deep Residual Correction Network is proposed by plugging one residual block into the source network along with the task-specific feature layer, which effectively enhances the adaptation from source to target and explicitly weakens the influence from the irrelevant source classes. Expand
Joint Adversarial Domain Adaptation
TLDR
A Joint Adversarial Domain Adaptation approach to simultaneously align domain-wise and class-wise distributions across source and target in a unified adversarial learning process, and can gain remarkable improvements over other state-of-the-art deep domain adaptation approaches. Expand
Deep Unsupervised Convolutional Domain Adaptation
TLDR
Deep Unsupervised Convolutional Domain Adaptation DUCDA method is proposed, which jointly minimizes the supervised classification loss of labeled source data and the unsupervised correlation alignment loss measured on both convolutional layers and fully connected layers. Expand
Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation
TLDR
This paper seeks the optimal projection via a novel relaxed domain-irrelevant clustering-promoting term that jointly bridges the cross-domain semantic gap and increases the intra-class compactness in both domains. Expand
Moment Matching for Multi-Source Domain Adaptation
TLDR
A new deep learning approach, Moment Matching for Multi-Source Domain Adaptation (M3SDA), which aims to transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions. Expand
Unsupervised Domain Adaptation with Residual Transfer Networks
TLDR
Empirical evidence shows that the new approach to domain adaptation in deep networks that can jointly learn adaptive classifiers and transferable features from labeled data in the source domain and unlabeledData in the target domain outperforms state of the art methods on standard domain adaptation benchmarks. Expand
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
TLDR
A novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks that achieves the state-of-the-art accuracy in the standard setting and the multi-source domain adaption scenario. Expand
Domain-Symmetric Networks for Adversarial Domain Adaptation
TLDR
This paper proposes a new domain adaptation method called Domain-Symmetric Networks (SymNets), which is based on a symmetric design of source and target task classifiers, based on which an additional classifier is constructed that shares with them its layer neurons. Expand
Adaptive Batch Normalization for practical domain adaptation
TLDR
This paper proposes a simple yet powerful remedy, called Adaptive Batch Normalization (AdaBN) to increase the generalization ability of a DNN, and demonstrates that the method is complementary with other existing methods and may further improve model performance. Expand
...
1
2
3
4
5
...