Fetching the paper…
Reading the bibliography…
Test-time adaptation (TTA) aims to improve the performance of source-domain pre-trained models on previously unseen, shifted target domains.
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.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
VQA: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Learning deep object detectors from 3d models
Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko · 2015
Earlier work this paper cites.
On rendering synthetic images for training an object detector
Artem Rozantsev, Vincent Lepetit, and Pascal Fua · 2015
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.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Augmented reality meets computer vision: Efficient data generation for urban driving scenes
Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars Mescheder, Andreas Geiger, and Carsten Rother · 2018
Earlier work this paper cites.
Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
Earlier work this paper cites.
Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Earlier work this paper cites.
Learning from synthetic data: Addressing domain shift for semantic segmentation
Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, and Rama Chellappa · 2018
Earlier work this paper cites.
Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach
Yuhua Chen, Wen Li, Xiaoran Chen, and Luc Van Gool · 2019
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
Cited alongside, same era.
Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
Cited alongside, same era.
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2020
Cited alongside, same era.
Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
Later among the works it cites.
Nerf-supervision: Learning dense object descriptors from neural radiance fields
Lin Yen-Chen, Pete Florence, Jonathan T Barron, Tsung-Yi Lin, Alberto Rodriguez, and Phillip Isola · 2022
Later among the works it cites.
Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
Later among the works it cites.
Synthetic data from diffusion models improves imagenet classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, and David J. Fleet · 2023
Later among the works it cites.
Openmmlab’s pre-training toolbox and benchmark
MMPreTrain Contributors · 2023
Later among the works it cites.
Back to the source: Diffusion-driven adaptation to test-time corruption
Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, and Dequan Wang · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
Cited alongside, same era.
Parameter-free online test-time adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto · 2022
Cited alongside, same era.
Test-time training with masked autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen, and Alexei Efros · 2022
Cited alongside, same era.
Later among the works it cites.
A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others
Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, and Mark Ibrahim · 2023
Later among the works it cites.
Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation
Quang Nguyen, Truong Vu, Anh Tran, and Khoi Nguyen · 2023
Later among the works it cites.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
Later among the works it cites.
Diffusion-tta: Test-time adaptation of discriminative models via generative feedback
Mihir Prabhudesai, Tsung-Wei Ke, Alexander Cong Li, Deepak Pathak, and Katerina Fragkiadaki · 2023
Later among the works it cites.
Robust test-time adaptation in dynamic scenarios
Longhui Yuan, Binhui Xie, and Shuang Li · 2023
Later among the works it cites.
Unitta: Unified benchmark and versatile framework towards realistic test-time adaptation
Chaoqun Du, Yulin Wang, Jiayi Guo, Yizeng Han, Jie Zhou, and Gao Huang · 2024
Closest in time.
Scaling laws of synthetic images for model training… for now
Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, and Yonglong Tian · 2024
Closest in time.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
Closest in time.
Universal test-time adaptation through weight ensembling, diversity weighting, and prior correction
Robert A Marsden, Mario Döbler, and Bin Yang · 2024
Closest in time.
Towards real-world test-time adaptation: Tri-net self-training with balanced normalization
Yongyi Su, Xun Xu, and Kui Jia · 2024
Closest in time.
Un-mixing test-time normalization statistics: Combatting label temporal correlation
Devavrat Tomar, Guillaume Vray, Jean-Philippe Thiran, and Behzad Bozorgtabar · 2024
Closest in time.
Gda: Generalized diffusion for robust test-time adaptation
Yun-Yun Tsai, Fu-Chen Chen, Albert YC Chen, Junfeng Yang, Che-Chun Su, Min Sun, and Cheng-Hao Kuo · 2024
Closest in time.