Open Compound Domain Adaptation

A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing works always assume that there exist clear distinctions between the domains, which is often not true in practice (e.g., changes in weather). We study an open compound domain adaptation (OCDA) problem, in which the target is a compound of multiple homogeneous domains without domain labels, reflecting realistic data collection from mixed and novel situations. We propose a new approach based on two technical insights into OCDA: 1) a curriculum domain adaptation strategy to bootstrap generalization across domains in a data-driven self-organizing fashion and 2) a memory module to increase the model's agility towards novel domains. Our experiments on digit classification, facial expression recognition, semantic segmentation, and reinforcement learning demonstrate the effectiveness of our approach.

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

[2]  Nikos Komodakis,et al.  Dynamic Few-Shot Visual Learning Without Forgetting , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  Luc Van Gool,et al.  Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding , 2018, ECCV.

[6]  Bo Wang,et al.  Moment Matching for Multi-Source Domain Adaptation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[7]  Kate Saenko,et al.  Domain Agnostic Learning with Disentangled Representations , 2019, ICML.

[8]  Trevor Darrell,et al.  Discovering Latent Domains for Multisource Domain Adaptation , 2012, ECCV.

[9]  Yongxin Yang,et al.  Learning to Generalize: Meta-Learning for Domain Generalization , 2017, AAAI.

[10]  Michael I. Jordan,et al.  Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.

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

[12]  Trevor Darrell,et al.  BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling , 2018, ArXiv.

[13]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[14]  Xiaoxiao Li,et al.  Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Samuel Rota Bulo,et al.  Inferring Latent Domains for Unsupervised Deep Domain Adaptation , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Trevor Darrell,et al.  Adapting to Continuously Shifting Domains , 2018, ICLR.

[17]  Alex ChiChung Kot,et al.  Domain Generalization with Adversarial Feature Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[19]  Xiaogang Wang,et al.  Fashion Landmark Detection in the Wild , 2016, ECCV.

[20]  Luc Van Gool,et al.  DLOW: Domain Flow for Adaptation and Generalization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Jason J. Corso,et al.  Latent Domains Modeling for Visual Domain Adaptation , 2014, AAAI.

[22]  Wen Li,et al.  Domain Generalization and Adaptation Using Low Rank Exemplar SVMs , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[24]  Xiaoou Tang,et al.  Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net , 2018, ECCV.

[25]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Huanhuan Yu,et al.  Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories , 2018, ArXiv.

[27]  Vladimir Pavlovic,et al.  Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach , 2018, IEEE Transactions on Image Processing.

[28]  Xiaofeng Liu,et al.  Confidence Regularized Self-Training , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

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

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

[31]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[32]  Bhiksha Raj,et al.  SphereFace: Deep Hypersphere Embedding for Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Fengmao Lv,et al.  Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[34]  Philip David,et al.  A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Barbara Caputo,et al.  AdaGraph: Unifying Predictive and Continuous Domain Adaptation Through Graphs , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Juergen Gall,et al.  Open Set Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[37]  Chong-Wah Ngo,et al.  Transferrable Prototypical Networks for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Dong Xu,et al.  Exploiting Low-Rank Structure from Latent Domains for Domain Generalization , 2014, ECCV.

[39]  Larry S. Davis,et al.  ACE: Adapting to Changing Environments for Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[40]  Liang Lin,et al.  Blending-Target Domain Adaptation by Adversarial Meta-Adaptation Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[42]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[43]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[45]  Stella X. Yu,et al.  Large-Scale Long-Tailed Recognition in an Open World , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[47]  Yang Zou,et al.  Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training , 2018, ArXiv.

[48]  Victor S. Lempitsky,et al.  Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.

[49]  Andrew Y. Ng,et al.  Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .

[50]  Tatsuya Harada,et al.  Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[51]  Kristen Grauman,et al.  Reshaping Visual Datasets for Domain Adaptation , 2013, NIPS.

[52]  Vladlen Koltun,et al.  Playing for Data: Ground Truth from Computer Games , 2016, ECCV.

[53]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[54]  Hexiang Hu,et al.  Synthesize Policies for Transfer and Adaptation across Tasks and Environments , 2019, NeurIPS.

[55]  Ming-Hsuan Yang,et al.  Learning to Adapt Structured Output Space for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[57]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.