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Machine learning methods strive to acquire a robust model during the training process that can effectively generalize to test samples, even in the presence of distribution shifts.
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Thorsten Joachims · 1999
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Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
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Semi-supervised learning by entropy minimization
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Non-stationary data sequence classification using online class priors estimation
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A survey on transfer learning
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A theory of learning from different domains
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Discriminative clustering by regularized information maximization
Andreas Krause, Pietro Perona, and Ryan Gomes · 2010
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Online domain adaptation of a pre-trained cascade of classifiers
Vidit Jain and Erik Learned-Miller · 2011
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Yuan Shi and Fei Sha · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Stability and hypothesis transfer learning
Ilja Kuzborskij and Francesco Orabona · 2013
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Learning categories from few examples with multi model knowledge transfer
Tatiana Tommasi, Francesco Orabona, and Barbara Caputo · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Classifier adaptation at prediction time
Amelie Royer and Christoph H Lampert · 2015
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Domain adaptation in the absence of source domain data
Boris Chidlovskii, Stephane Clinchant, and Gabriela Csurka · 2016
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Transductive adaptation of black box predictions
Stéphane Clinchant, Boris Chidlovskii, and Gabriela Csurka · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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No more discrimination: Cross city adaptation of road scene segmenters
Yi-Hsin Chen, Wei-Yu Chen, Yu-Ting Chen, Bo-Cheng Tsai, Yu-Chiang Frank Wang, and Min Sun · 2017
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Fast generalized distillation for semi-supervised domain adaptation
Shuang Ao, Xiang Li, and Charles Ling · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Unsupervised domain adaptation with random walks on target labelings
Twan van Laarhoven and Elena Marchiori · 2017
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Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Demystifying neural style transfer
Yanghao Li, Naiyan Wang, Jiaying Liu, and Xiaodi Hou · 2017
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Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2017
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Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
Sergey Ioffe · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Source free domain adaptation using an off-the-shelf classifier
Arun Reddy Nelakurthi, Ross Maciejewski, and Jingrui He · 2018
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Learning to generalize: meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Understanding measures of uncertainty for adversarial example detection
Lewis Smith and Yarin Gal · 2018
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Geometry-aware learning of maps for camera localization
Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz · 2018
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“zero-shot” super-resolution using deep internal learning
Assaf Shocher, Nadav Cohen, and Michal Irani · 2018
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Kitting in the wild through online domain adaptation
Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, and Barbara Caputo · 2018
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Deep face detector adaptation without negative transfer or catastrophic forgetting
Muhammad Abdullah Jamal, Haoxiang Li, and Boqing Gong · 2018
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A review of domain adaptation without target labels
Wouter M Kouw and Marco Loog · 2019
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Domain adaptation for semantic segmentation with maximum squares loss
Minghao Chen, Hongyang Xue, and Deng Cai · 2019
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Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
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Domain generalization by solving jigsaw puzzles
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation
Jian Liang, Ran He, Zhenan Sun, and Tieniu Tan · 2019
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Nlnl: Negative learning for noisy labels
Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Avital Oliver, Nicolas Papernot, and Colin Raffel · 2019
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Universal domain adaptation
Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2019
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Learning to generalize one sample at a time with self-supervision
Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
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Dynamic scale inference by entropy minimization
Dequan Wang, Evan Shelhamer, Bruno Olshausen, and Trevor Darrell · 2019
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Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 2019
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Semantic photo manipulation with a generative image prior
David Bau, Hendrik Strobelt, William Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, and Antonio Torralba · 2019
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Episodic training for domain generalization
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M Hospedales · 2019
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Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera
Yuhua Chen, Cordelia Schmid, and Cristian Sminchisescu · 2019
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Automatic adaptation of object detectors to new domains using self-training
Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, and Erik Learned-Miller · 2019
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Distilled person re-identification: Towards a more scalable system
Ancong Wu, Wei-Shi Zheng, Xiaowei Guo, and Jian-Huang Lai · 2019
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt · 2020
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Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, and R Venkatesh Babu · 2020
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Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
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A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J Cook · 2020
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Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian · 2020
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Minimum class confusion for versatile domain adaptation
Ying Jin, Ximei Wang, Mingsheng Long, and Jianmin Wang · 2020
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Adversarial style mining for one-shot unsupervised domain adaptation
Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, and Yi Yang · 2020
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Test-time unsupervised domain adaptation
Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H Sudre, Mark S Graham, Parashkev Nachev, and M Jorge Cardoso · 2020
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Multi-step online unsupervised domain adaptation
JH Moon, Debasmit Das, and CS George Lee · 2020
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Camera on-boarding for person re-identification using hypothesis transfer learning
Sk Miraj Ahmed, Aske R Lejbolle, Rameswar Panda, and Amit K Roy-Chowdhury · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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Learning loss for test-time augmentation
Ildoo Kim, Younghoon Kim, and Sungwoong Kim · 2020
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Source free domain adaptation with image translation
Yunzhong Hou and Liang Zheng · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le · 2020
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Generative pseudo-label refinement for unsupervised domain adaptation
Pietro Morerio, Riccardo Volpi, Ruggero Ragonesi, and Vittorio Murino · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Unsupervised domain adaptation in the absence of source data
Roshni Sahoo, Divya Shanmugam, and John Guttag · 2020
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Fda: Fourier domain adaptation for semantic segmentation
Yanchao Yang and Stefano Soatto · 2020
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A balanced and uncertainty-aware approach for partial domain adaptation
Jian Liang, Yunbo Wang, Dapeng Hu, Ran He, and Jiashi Feng · 2020
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Towards inheritable models for open-set domain adaptation
Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, and R Venkatesh Babu · 2020
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Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation
Amr Alexandari, Anshul Kundaje, and Avanti Shrikumar · 2020
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Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2020
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Fast adaptation to super-resolution networks via meta-learning
Seobin Park, Jinsu Yoo, Donghyeon Cho, Jiwon Kim, and Tae Hyun Kim · 2020
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One-shot unsupervised cross-domain detection
Antonio D’Innocente, Francesco Cappio Borlino, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2020
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Inference stage optimization for cross-scenario 3d human pose estimation
Jianfeng Zhang, Xuecheng Nie, and Jiashi Feng · 2020
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Online adaptation for consistent mesh reconstruction in the wild
Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, and Jan Kautz · 2020
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Learning to optimize domain specific normalization for domain generalization
Seonguk Seo, Yumin Suh, Dongwan Kim, Geeho Kim, Jongwoo Han, and Bohyung Han · 2020
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Image-adaptive gan based reconstruction
Shady Abu Hussein, Tom Tirer, and Raja Giryes · 2020
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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Adaptive risk minimization: Learning to adapt to domain shift
Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn · 2021
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Test-time classifier adjustment module for model-agnostic domain generalization
Yusuke Iwasawa and Yutaka Matsuo · 2021
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Data-free knowledge transfer: A survey
Yuang Liu, Wei Zhang, Jun Wang, and Jianyong Wang · 2021
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2021
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Better aggregation in test-time augmentation
Divya Shanmugam, Davis Blalock, Guha Balakrishnan, and John Guttag · 2021
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Enhancing adversarial robustness via test-time transformation ensembling
Juan C Pérez, Motasem Alfarra, Guillaume Jeanneret, Laura Rueda, Ali Thabet, Bernard Ghanem, and Pablo Arbeláez · 2021
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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Shiqi Yang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
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Domain adaptive semantic segmentation without source data
Fuming You, Jingjing Li, Lei Zhu, Zhi Chen, and Zi Huang · 2021
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Augmented self-labeling for source-free unsupervised domain adaptation
Hao Yan, Yuhong Guo, and Chunsheng Yang · 2021
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Generalized source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
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Uncertainty reduction for model adaptation in semantic segmentation
Prabhu Teja Sivaprasad and François Fleuret · 2021
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Umad: Universal model adaptation under domain and category shift
Jian Liang, Dapeng Hu, Jiashi Feng, and Ran He · 2021
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Imbalanced source-free domain adaptation
Xinhao Li, Jingjing Li, Lei Zhu, Guoqing Wang, and Zi Huang · 2021
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Source-free domain adaptation via avatar prototype generation and adaptation
Zhen Qiu, Yifan Zhang, Hongbin Lin, Shuaicheng Niu, Yanxia Liu, Qing Du, and Mingkui Tan · 2021
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Domain adaptation without source data
Youngeun Kim, Donghyeon Cho, Kyeongtak Han, Priyadarshini Panda, and Sungeun Hong · 2021
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A free lunch for unsupervised domain adaptive object detection without source data
Xianfeng Li, Weijie Chen, Di Xie, Shicai Yang, Peng Yuan, Shiliang Pu, and Yueting Zhuang · 2021
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Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling
Cheng Chen, Quande Liu, Yueming Jin, Qi Dou, and Pheng-Ann Heng · 2021
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Visualizing adapted knowledge in domain transfer
Yunzhong Hou and Liang Zheng · 2021
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Model adaptation through hypothesis transfer with gradual knowledge distillation
Song Tang, Yuji Shi, Zhiyuan Ma, Jian Li, Jianzhi Lyu, Qingdu Li, and Jianwei Zhang · 2021
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Towards cross-environment human activity recognition based on radar without source data
Single-domain generalization in medical image segmentation via test-time adaptation from shape dictionary
Quande Liu, Cheng Chen, Qi Dou, and Pheng-Ann Heng · 2022
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Self-constrained inference optimization on structural groups for human pose estimation
Zhehan Kan, Shuoshuo Chen, Zeng Li, and Zhihai He · 2022
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Variational on-the-fly personalization
Jangho Kim, Jun-Tae Lee, Simyung Chang, and Nojun Kwak · 2022
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Towards multi-domain single image dehazing via test-time training
Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, and Keyan Wang · 2022
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Sketch3t: Test-time training for zero-shot sbir
Aneeshan Sain, Ayan Kumar Bhunia, Vaishnav Potlapalli, Pinaki Nath Chowdhury, Tao Xiang, and Yi-Zhe Song · 2022
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Dynamic domain generalization
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Zhongping Cao, Zhenchang Li, Xuemei Guo, and Guoli Wang · 2021
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Confident anchor-induced multi-source free domain adaptation
Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, and Tongliang Liu · 2021
Cited alongside, same era.
Domain adaptation with auxiliary target domain-oriented classifier
Jian Liang, Dapeng Hu, and Jiashi Feng · 2021
Cited alongside, same era.
Xin Luo, Wei Chen, Yusong Tan, Chen Li, Yulin He, and Xiaogang Jia · 2021
Cited alongside, same era.
Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu · 2021
Cited alongside, same era.
Unsupervised domain adaptation of black-box source models
Haojian Zhang, Yabin Zhang, Kui Jia, and Lei Zhang · 2021
Cited alongside, same era.
Generalize then adapt: Source-free domain adaptive semantic segmentation
Jogendra Nath Kundu, Akshay Kulkarni, Amit Singh, Varun Jampani, and R Venkatesh Babu · 2021
Cited alongside, same era.
Zhishu Sun, Zhifeng Shen, Luojun Lin, Yuanlong Yu, Zhifeng Yang, Shicai Yang, and Weijie Chen · 2022
Later among the works it cites.
Pada: Example-based prompt learning for on-the-fly adaptation to unseen domains
Eyal Ben-David, Nadav Oved, and Roi Reichart · 2022
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Learning to generalize across domains on single test samples
Zehao Xiao, Xiantong Zhen, Ling Shao, and Cees GM Snoek · 2022
Later among the works it cites.
Mimic embedding via adaptive aggregation: Learning generalizable person re-identification
Boqiang Xu, Jian Liang, Lingxiao He, and Zhenan Sun · 2022
Later among the works it cites.
Test-time fourier style calibration for domain generalization
Xingchen Zhao, Chang Liu, Anthony Sicilia, Seong Jae Hwang, and Yun Fu · 2022
Later among the works it cites.
Self-supervised test-time adaptation for medical image segmentation
Hao Li, Han Liu, Dewei Hu, Jiacheng Wang, Hans Johnson, Omar Sherbini, Francesco Gavazzi, Russell D’Aiello, Adeline Vanderver, Jeffrey Long, Paulsen Jane, and Ipek Oguz · 2022
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Re-using adversarial mask discriminators for test-time training under distribution shifts
Gabriele Valvano, Andrea Leo, and Sotirios A Tsaftaris · 2022
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Combating adversaries with anti-adversaries
Motasem Alfarra, Juan C Pérez, Ali Thabet, Adel Bibi, Philip HS Torr, and Bernard Ghanem · 2022
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Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
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The norm must go on: Dynamic unsupervised domain adaptation by normalization
M Jehanzeb Mirza, Jakub Micorek, Horst Possegger, and Horst Bischof · 2022
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Robustifying vision transformer without retraining from scratch by test-time class-conditional feature alignment
Takeshi Kojima, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Cd-tta: Compound domain test-time adaptation for semantic segmentation
Junha Song, Kwanyong Park, Inkyu Shin, Sanghyun Woo, and In So Kweon · 2022
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Tao Yang, Shenglong Zhou, Yuwang Wang, Yan Lu, and Nanning Zheng · 2022
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Note: Robust continual test-time adaptation against temporal correlation
Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, and Sung-Ju Lee · 2022
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Generalizable model-agnostic semantic segmentation via target-specific normalization
Jian Zhang, Lei Qi, Yinghuan Shi, and Yang Gao · 2022
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Multi-step test-time adaptation with entropy minimization and pseudo-labeling
Hiroaki Kingetsu, Kenichi Kobayashi, Yoshihiro Okawa, Yasuto Yokota, and Katsuhito Nakazawa · 2022
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Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi, and Sungrack Yun · 2022
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Uncertainty-aware adaptation for self-supervised 3d human pose estimation
Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM, Varun Jampani, Anirban Chakraborty, and R Venkatesh Babu · 2022
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Online adaptive personalization for face anti-spoofing
Davide Belli, Debasmit Das, Bence Major, and Fatih Porikli · 2022
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Mm-tta: Multi-modal test-time adaptation for 3d semantic segmentation
Inkyu Shin, Yi-Hsuan Tsai, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Sparsh Garg, In So Kweon, and Kuk-Jin Yoon · 2022
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Dltta: Dynamic learning rate for test-time adaptation on cross-domain medical images
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