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Mohammad Havaei | Yoshua Bengio | Xiang Jiang | Qicheng Lao | Yoshua Bengio | Mohammad Havaei | Xiang Jiang | Qicheng Lao
[1] João Gama,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[2] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[4] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[5] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[6] Trevor Darrell,et al. Adapting to Continuously Shifting Domains , 2018, ICLR.
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] 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).
[9] Lantao Yu,et al. Improving Unsupervised Domain Adaptation with Variational Information Bottleneck , 2020, ECAI.
[10] Gregory Ditzler,et al. Learning in Nonstationary Environments: A Survey , 2015, IEEE Computational Intelligence Magazine.
[11] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[12] Yee Whye Teh,et al. Progress & Compress: A scalable framework for continual learning , 2018, ICML.
[13] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Rajat Raina,et al. Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.
[15] Richard E. Turner,et al. Variational Continual Learning , 2017, ICLR.
[16] Christoph H. Lampert,et al. iCaRL: Incremental Classifier and Representation Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Luc Van Gool,et al. DLOW: Domain Flow for Adaptation and Generalization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[19] Jiwon Kim,et al. Continual Learning with Deep Generative Replay , 2017, NIPS.
[20] Marc'Aurelio Ranzato,et al. Gradient Episodic Memory for Continual Learning , 2017, NIPS.
[21] J. C. Schlimmer,et al. Incremental learning from noisy data , 2004, Machine Learning.
[22] Michael I. Jordan,et al. Conditional Adversarial Domain Adaptation , 2017, NeurIPS.
[23] Zijian Li,et al. Learning Disentangled Semantic Representation for Domain Adaptation , 2019, IJCAI.
[24] Junqing Yu,et al. Significance-Aware Information Bottleneck for Domain Adaptive Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[25] Davide Maltoni,et al. Latent Replay for Real-Time Continual Learning , 2019, ArXiv.
[26] David Rolnick,et al. Experience Replay for Continual Learning , 2018, NeurIPS.
[27] L. Gool,et al. DLOW: Domain Flow for Adaptation and Generalization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Yuchen Zhang,et al. Bridging Theory and Algorithm for Domain Adaptation , 2019, ICML.
[29] Razvan Pascanu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[30] Philip S. Yu,et al. A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions , 2007, SDM.
[31] Alexander A. Alemi,et al. Deep Variational Information Bottleneck , 2017, ICLR.
[32] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[33] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[34] Surya Ganguli,et al. Continual Learning Through Synaptic Intelligence , 2017, ICML.
[35] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[36] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[37] Gerhard Widmer,et al. Effective Learning in Dynamic Environments by Explicit Context Tracking , 1993, ECML.
[38] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[39] Gerhard Widmer,et al. Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.
[40] Kate Saenko,et al. Domain Agnostic Learning with Disentangled Representations , 2019, ICML.
[41] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[43] Bogdan Raducanu,et al. Memory Replay GANs: Learning to Generate New Categories without Forgetting , 2018, NeurIPS.
[44] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[45] Alex Bewley,et al. Incremental Adversarial Domain Adaptation for Continually Changing Environments , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[46] Alexey Tsymbal,et al. The problem of concept drift: definitions and related work , 2004 .
[47] Jakub M. Tomczak,et al. DIVA: Domain Invariant Variational Autoencoders , 2019, DGS@ICLR.
[48] Trevor Darrell,et al. Continuous Manifold Based Adaptation for Evolving Visual Domains , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[49] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[50] B. McNaughton,et al. Replay of Neuronal Firing Sequences in Rat Hippocampus During Sleep Following Spatial Experience , 1996, Science.
[51] Anthony V. Robins,et al. Catastrophic Forgetting, Rehearsal and Pseudorehearsal , 1995, Connect. Sci..