Fetching the paper…
Reading the bibliography…
Training state-of-the-art vision models has become prohibitively expensive for researchers and practitioners.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
Earlier work this paper cites.
The pareto, zipf and other power laws
William J Reed · 2001
Earlier work this paper cites.
The concave-convex procedure (CCCP)
Alan L. Yuille and Anand Rangarajan · 2001
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
When training and test sets are different: characterizing learning transfer
Amos Storkey · 2009
Earlier work this paper cites.
Semi-Supervised Learning
Olivier Chapelle, Bernhard Schlkopf, and Alexander Zien · 2010
Earlier work this paper cites.
Undoing the damage of dataset bias
Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei A Efros, and Antonio Torralba · 2012
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.
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
Earlier work this paper cites.
Multi-view domain generalization for visual recognition
Li Niu, Wen Li, and Dong Xu · 2015
Earlier work this paper cites.
Visual domain adaptation: A survey of recent advances
Vishal M Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2015
Earlier work this paper cites.
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, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Domain adaptation in the absence of source domain data
Boris Chidlovskii, Stephane Clinchant, and Gabriela Csurka · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Variational recurrent adversarial deep domain adaptation
Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, and Yan Liu · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Earlier work this paper cites.
Domain adaptation for visual applications: A comprehensive survey
Gabriela Csurka · 2017
Earlier work this paper cites.
Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Earlier work this paper cites.
Adaptive batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Xiaodi Hou, and Jiaying Liu · 2018
Cited alongside, same era.
Spectralnet: Spectral clustering using deep neural networks
Uri Shaham, Kelly Stanton, Henry Li, Ronen Basri, Boaz Nadler, and Yuval Kluger · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese · 2018
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
Cited alongside, same era.
Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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.
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
Later among the works it cites.
Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
Later among the works it cites.
Frustratingly simple domain generalization via image stylization
Nathan Somavarapu, Chih-Yao Ma, and Zsolt Kira · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation
Jian Liang, Ran He, Zhenan Sun, and Tieniu Tan · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Structured domain randomization: Bridging the reality gap by context-aware synthetic data
Aayush Prakash, Shaad Boochoon, Mark Brophy, David Acuna, Eric Cameracci, Gavriel State, Omer Shapira, and Stan Birchfield · 2019
Cited alongside, same era.
Multi-component image translation for deep domain generalization
Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
Later among the works it cites.
A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J Cook · 2020
Later among the works it cites.
Deep domain-adversarial image generation for domain generalisation
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang · 2020
Later among the works it cites.
Laplacian regularized few-shot learning
Imtiaz Ziko, Jose Dolz, Eric Granger, and Ismail Ben Ayed · 2020
Later among the works it cites.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Later among the works it cites.
Limitations of post-hoc feature alignment for robustness
Collin Burns and Jacob Steinhardt · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Later among the works it cites.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
Later among the works it cites.
Carbon emissions and large neural network training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
Later among the works it cites.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
Later among the works it cites.
Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin · 2021
Later among the works it cites.
Self-supervised test-time adaptation on video data
Fatemeh Azimi, Sebastian Palacio, Federico Raue, Jörn Hees, Luca Bertinetto, and Andreas Dengel · 2022
Closest in time.