Fetching the paper…
Reading the bibliography…
We study a new highly-practical problem setting that enables resource-constrained edge devices to adapt a pre-trained model to their local data distributions.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Yuan Shi and Fei Sha · 2012
Earlier work this paper cites.
Continuous manifold based adaptation for evolving visual domains
Judy Hoffman, Trevor Darrell, and Kate Saenko · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
EMNIST: an extension of MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Meta-SGD: learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S Zemel · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
LEAF: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Earlier work this paper cites.
Learning to compare: relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H S Torr, and Timothy M Hospedales · 2018
Earlier work this paper cites.
How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 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.
Cross attention network for few-shot classification
Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen · 2019
Cited alongside, same era.
Episodic training for domain generalization
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M Hospedales · 2019
Cited alongside, same era.
Domain agnostic learning with disentangled representations
Xingchao Peng, Zijun Huang, Ximeng Sun, and Kate Saenko · 2019
Cited alongside, same era.
The iWildCam 2020 competition dataset
Sara Beery, Elijah Cole, and Arvi Gjoka · 2020
Cited alongside, same era.
CrossTransformers: spatially-aware few-shot transfer
Carl Doersch, Ankush Gupta, and Andrew Zisserman · 2020
Cited alongside, same era.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
Cited alongside, same era.
Meta-learning in neural networks: a survey
Timothy M Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J Storkey · 2021
Later among the works it cites.
Source-free domain adaptation via distributional alignment by matching batch normalization statistics
Masato Ishii and Masashi Sugiyama · 2021
Later among the works it cites.
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 Earnshaw, Imran Haque, Sara M Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
Later among the works it cites.
Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
Jannik Kossen, Neil Band, Clare Lyle, Aidan N Gomez, Tom Rainforth, and Yarin Gal · 2021
Later among the works it cites.
A review of domain adaptation without target labels
Wouter M. Kouw and Marco Loog · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Open compound domain adaptation
Ziwei Liu, Zhongqi Miao, Xingang Pan, Xiaohang Zhan, Dahua Lin, Stella X. Yu, and Boqing Gong · 2020
Cited alongside, same era.
Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
Cited alongside, same era.
Few-shot learning via embedding adaptation with set-to-set functions
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
Cited alongside, same era.
CrossViT: Cross-attention multi-scale vision transformer for image classification
Chun-Fu Chen, Quanfu Fan, and Rameswar Panda · 2021
Cited alongside, same era.
Inferring latent domains for unsupervised deep domain adaptation
Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulo, Barbara Caputo, and Elisa Ricci · 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.
Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
Later among the works it cites.
Adaptive risk minimization: learning to adapt to domain shift
Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn · 2021
Later among the works it cites.
Visual domain adaptation in the deep learning era
Gabriela Csurka, Timothy M Hospedales, Mathieu Salzmann, and Tatiana Tommasi · 2022
Closest in time.
Visual representation learning over latent domains
Lucas Deecke, Timothy Hospedales, and Hakan Bilen · 2022
Closest in time.
Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
Closest in time.
Source-free open compound domain adaptation in semantic segmentation
Yuyang Zhao, Zhun Zhong, Zhiming Luo, Gim Hee Lee, and Nicu Sebe · 2022
Closest in time.