Domain adaptation is a viable solution for deep learning with small data. However, domain models trained on data sensitive information may be violation of personal privacy. In this article, we proposed unsupervised adaptation, called DP-CUDA, which based differentially private gradient projection and contradistinguisher. Compared the traditional process, DP-CUDA involves searching domain-invari...

Unsupervised domain adaptation, which involves transferring knowledge from a label-rich source to an unlabeled target domain, can be used substantially reduce annotation costs in the field of object detection. In this study, we demonstrate that adversarial training employed as new approach for unsupervised adaptation. Specifically, establish adversarially trained detectors achieve improved dete...

Conventional machine learning relies on two presumptions: (1) the training and testing datasets follow same independent distribution, (2) an adequate quantity of samples is essential for achieving optimal model performance during training. Nevertheless, meeting these assumptions can be challenging in real-world scenarios. Domain adaptation (DA) a subfield transfer that focuses reducing distribu...

Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between source and target domains through refining feature generator, in order learn a better alignment two domains. This minimization can be achieved via classifier detect target-domain features that are divergent from source-domain features. However, when optimizing such domain-classification discrepancy...

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain centroids. However, inner-class compactness and underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out subtype-aware align...

Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a labeled source domain an unlabeled target domain. UDA has been extensively studied in the computer vision literature. Deep networks have shown be vulnerable adversarial attacks. However, very little focus is devoted improving robustness of deep models, causing serious concerns about model reliability. Adversarial Trai...

Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source to an unlabelled target domain. Recently, adversarial learning with bi-classifier has been proven effective in pushing cross-domain distributions close. Prior approaches typically leverage disagreement between learn transferable representations, however, they often neglect classifier dete...