A Sample Selection Approach for Universal Domain Adaptation

We study the problem of unsupervised domain adaption in the universal scenario, in which only some of the classes are shared between the source and target domains. We present a scoring scheme that is effective in identifying the samples of the shared classes. The score is used to select which samples in the target domain to pseudo-label during training. Another loss term encourages diversity of labels within each batch. Taken together, our method is shown to outperform, by a sizable margin, the current state of the art on the literature benchmarks.

[1]  Silvio Savarese,et al.  Learning Transferrable Representations for Unsupervised Domain Adaptation , 2016, NIPS.

[2]  Michael I. Jordan,et al.  Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.

[3]  Jing Zhang,et al.  Importance Weighted Adversarial Nets for Partial Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Juergen Gall,et al.  Open Set Domain Adaptation for Image and Action Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Tatsuya Harada,et al.  Open Set Domain Adaptation by Backpropagation , 2018, ECCV.

[7]  Sheng-De Wang,et al.  Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Fabio Maria Carlucci,et al.  From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[9]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Carlos D. Castillo,et al.  Generate to Adapt: Aligning Domains Using Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Kate Saenko,et al.  VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Jianmin Wang,et al.  Partial Transfer Learning with Selective Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  Changick Kim,et al.  Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation , 2019, BMVC.

[15]  Geoffrey French,et al.  Self-ensembling for visual domain adaptation , 2017, ICLR.

[16]  David J. Kriegman,et al.  Image to Image Translation for Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Shang-Hong Lai,et al.  AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation , 2018, ECCV.

[18]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

[19]  Dong Xu,et al.  Collaborative and Adversarial Network for Unsupervised Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[21]  Shiguang Shan,et al.  Duplex Generative Adversarial Network for Unsupervised Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Dumitru Erhan,et al.  Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Michael I. Jordan,et al.  Universal Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Sethuraman Panchanathan,et al.  Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Silvio Savarese,et al.  Adversarial Feature Augmentation for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[26]  Yoshua Bengio,et al.  Semi-supervised Learning by Entropy Minimization , 2004, CAP.

[27]  Stefano Ermon,et al.  A DIRT-T Approach to Unsupervised Domain Adaptation , 2018, ICLR.

[28]  Taesung Park,et al.  CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.

[29]  Tatsuya Harada,et al.  Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.

[30]  Jianmin Wang,et al.  Partial Adversarial Domain Adaptation , 2018, ECCV.