Fetching the paper…
Reading the bibliography…
Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation.
The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
Earlier work this paper cites.
Tangent prop-a formalism for specifying selected invariances in an adaptive network
Patrice Simard, Bernard Victorri, Yann LeCun, and John S Denker · 1991
Earlier work this paper cites.
Supervised and unsupervised discretization of continuous features
James Dougherty, Ron Kohavi, and Mehran Sahami · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Dataset Shift in Machine Learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Neil D Lawrence, and Anton Schwaighofer · 2009
Earlier work this paper cites.
When training and test sets are different: characterising learning transfer
Amos J Storkey · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Earlier work this paper cites.
Impossibility theorems for domain adaptation
Shai Ben David, Tyler Lu, Teresa Luu, and Dávid Pál · 2010
Earlier work this paper cites.
Discriminative clustering by regularized information maximization
Andreas Krause, Pietro Perona, and Ryan Gomes · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Earlier work this paper cites.
Contour detection and hierarchical image segmentation
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
Earlier work this paper cites.
A unifying view on dataset shift in classification
Jose G Moreno-Torres, Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V Chawla, and Francisco Herrera · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using Bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Cited alongside, same era.
Domain adaptation in the absence of source domain data
Boris Chidlovskii, Stéphane Clinchant, and Gabriela Csurka · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Later among the works it cites.
Benchmarking robustness in object detection: Autonomous driving when winter is coming
C. Michaelis, B. Mitzkus, R. Geirhos, E. Rusak, O. Bringmann, A. S. Ecker, M. Bethge, and W. Brendel · 2019
Later among the works it cites.
MNIST-C: A robustness benchmark for computer vision
Norman Mu and Justin Gilmer · 2019
Later among the works it cites.
Effects of degradations on deep neural network architectures
Prasun Roy, Subhankar Ghosh, Saumik Bhattacharya, and Umapada Pal · 2019
Later among the works it cites.
Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep CORAL: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Cited alongside, same era.
EMNIST: an extension of MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Cited alongside, same era.
Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing
Ehab A AlBadawy, Ashirbani Saha, and Maciej A Mazurowski · 2018
Cited alongside, same era.
Later among the works it cites.
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
David Tellez, Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, and Jeroen van der Laak · 2019
Later among the works it cites.
Towards robust CNN-based object detection through augmentation with synthetic rain variations
Georg Volk, Stefan Müller, Alexander von Bernuth, Dennis Hospach, and Oliver Bringmann · 2019
Later among the works it cites.
Towards accurate model selection in deep unsupervised domain adaptation
Kaichao You, Ximei Wang, Mingsheng Long, and Michael Jordan · 2019
Later among the works it cites.
A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy
Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, and Laura M. Vardoulakis · 2020
Later among the works it cites.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
Later among the works it cites.
Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
Later among the works it cites.
Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
Later among the works it cites.
Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
Later among the works it cites.
Generative pseudo-label refinement for unsupervised domain adaptation
Pietro Morerio, Riccardo Volpi, Ruggero Ragonesi, and Vittorio Murino · 2020
Later among the works it cites.
A flexible selection scheme for minimum-effort transfer learning
Amélie Royer and Christoph Lampert · 2020
Later among the works it cites.
On robustness and transferability of convolutional neural networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, et al · 2021
Closest in time.
Derivations for linear algebra and optimization, 2007
John Duchi · 2021
Closest in time.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
Closest in time.
Masato Ishii and Masashi Sugiyama · 2021
Closest in time.
WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
Closest in time.
Domain impression: A source data free domain adaptation method
Vinod K Kurmi, Venkatesh K Subramanian, and Vinay P Namboodiri · 2021
Closest in time.
Unsupervised model adaptation for continual semantic segmentation
Serban Stan and Mohammad Rostami · 2021
Closest in time.
TENT: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
Closest in time.
SoFA: Source-data-free feature alignment for unsupervised domain adaptation
Hao-Wei Yeh, Baoyao Yang, Pong C Yuen, and Tatsuya Harada · 2021
Closest in time.