Fetching the paper…
Reading the bibliography…
Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution.
Some methods of speeding up the convergence of iteration methods
Boris T Polyak · 1964
Earlier work this paper cites.
Natural gradient works efficiently in learning
Shunichi Amari · 1998
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course , volume 87
Yurii Nesterov · 2003
Earlier work this paper cites.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
Earlier work this paper cites.
Labelme: a database and web-based tool for image annotation
Bryan C Russell, Antonio Torralba, Kevin P Murphy, and William T Freeman · 2008
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 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.
Exploiting hierarchical context on a large database of object categories
Myung Jin Choi, Joseph J Lim, Antonio Torralba, and Alan S Willsky · 2010
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.
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
Masashi Sugiyama and Motoaki Kawanabe · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Domain generalization for object recognition with multi-task autoencoders
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Probabilistic model for code with decision trees
Veselin Raychev, Pavol Bielik, and Martin Vechev · 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.
Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Earlier work this paper cites.
The marginal value of adaptive gradient methods in machine learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, and Benjamin Recht · 2017
Earlier work this paper cites.
Dissecting adam: The sign, magnitude and variance of stochastic gradients
Lukas Balles and Philipp Hennig · 2018
Earlier work this paper cites.
From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Peter Bandi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, et al · 2018
Earlier work this paper cites.
Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
On the convergence of adam and beyond
Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar · 2018
Cited alongside, same era.
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.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Later among the works it cites.
On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2020
Later among the works it cites.
Luke Metz, Niru Maheswaranathan, C Daniel Freeman, Ben Poole, and Jascha Sohl-Dickstein · 2020
Later among the works it cites.
Neha S Wadia, Daniel Duckworth, Samuel S Schoenholz, Ethan Dyer, and Jascha Sohl-Dickstein · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
Cited alongside, same era.
On empirical comparisons of optimizers for deep learning
Dami Choi, Christopher J Shallue, Zachary Nado, Jaehoon Lee, Chris J Maddison, and George E Dahl · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Adaptive versus standard descent methods and robustness against adversarial examples
Marc Khoury · 2019
Cited alongside, same era.
Mnist-c: A robustness benchmark for computer vision
Norman Mu and Justin Gilmer · 2019
Cited alongside, same era.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
Cited alongside, same era.
Later among the works it cites.
Using publicly available satellite imagery and deep learning to understand economic well-being in africa
Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke · 2020
Later among the works it cites.
Swad: Domain generalization by seeking flat minima
Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park · 2021
Later among the works it cites.
Global wheat head dataset 2021: an update to improve the benchmarking wheat head localization with more diversity, 2021
Etienne David, Mario Serouart, Daniel Smith, Simon Madec, Kaaviya Velumani, Shouyang Liu, Xu Wang, Francisco Pinto Espinosa, Shahameh Shafiee, Izzat S. A. Tahir, Hisashi Tsujimoto, Shuhei Nasuda, Bangyou Zheng, Norbert Kichgessner, Helge Aasen, Andreas Hund, Pouria Sadhegi-Tehran, Koichi Nagasawa, Goro Ishikawa, Sebastien Dandrifosse, Alexis Carlier, Benoit Mercatoris, Ken Kuroki, Haozhou Wang, Masanori Ishii, Minhajul A. Badhon, Curtis Pozniak, David Shaner LeBauer, Morten Lilimo, Jesse Poland, Scott Chapman, Benoit de Solan, Frederic Baret, Ian Stavness, and Wei Guo · 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.
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, et al · 2021
Later among the works it cites.
Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
Later among the works it cites.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Later among the works it cites.
Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
Later among the works it cites.
Understanding the failure modes of out-of-distribution generalization
Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur · 2021
Later among the works it cites.
Descending through a crowded valley-benchmarking deep learning optimizers
Robin M Schmidt, Frank Schneider, and Philipp Hennig · 2021
Later among the works it cites.
Towards out-of-distribution generalization: A survey
Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui · 2021
Later among the works it cites.
Noise or signal: The role of image backgrounds in object recognition
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2021
Later among the works it cites.
Agreement-on-the-line: Predicting the performance of neural networks under distribution shift
Christina Baek, Yiding Jiang, Aditi Raghunathan, and Zico Kolter · 2022
Closest in time.
Id and ood performance are sometimes inversely correlated on real-world datasets, 2022
Damien Teney, Yong Lin, Seong Joon Oh, and Ehsan Abbasnejad · 2022
Closest in time.
Assaying out-of-distribution generalization in transfer learning
Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, et al · 2022
Closest in time.
Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
Closest in time.
Difan Zou, Yuan Cao, Yuanzhi Li, and Quanquan Gu · 2022
Closest in time.
Pareto invariant risk minimization: Towards mitigating the optimization dilemma in out-of-distribution generalization
Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, MA KAILI, Han Yang, Peilin Zhao, Bo Han, and James Cheng · 2023
Closest in time.