no code implementations • 12 Oct 2023 • Junyu Gao, Xinhong Ma, Changsheng Xu
Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting their applications in the scenarios that require safe and controllable model decisions.
no code implementations • ICCV 2021 • Xinhong Ma, Junyu Gao, Changsheng Xu
This paper proposes a new paradigm for unsupervised domain adaptation, termed as Active Universal Domain Adaptation (AUDA), which removes all label set assumptions and aims for not only recognizing target samples from source classes but also inferring those from target-private classes by using active learning to annotate a small budget of target data.
no code implementations • CVPR 2019 • Xinhong Ma, Tianzhu Zhang, Changsheng Xu
Different from existing methods, we propose an end-to-end Graph Convolutional Adversarial Network (GCAN) for unsupervised domain adaptation by jointly modeling data structure, domain label, and class label in a unified deep framework.