Fetching the paper…
Reading the bibliography…
While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t.
Arjovsky, M.; Bottou, L.; Gulrajani, I.; and Lopez-Paz, D. 2019 · 1907
Earlier work this paper cites.
Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
Robins, J. M.; Rotnitzky, A.; and Zhao, L. P. 1994 · 1994
Earlier work this paper cites.
Invariant Risk Minimization Games
Ahuja, K.; Shanmugam, K.; Varshney, K.; and Dhurandhar, A. 2020 · 2002
Earlier work this paper cites.
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Krueger, D.; Caballero, E.; Jacobsen, J.-H.; Zhang, A.; Binas, J.; Priol, R. L.; and Courville, A. 2020 · 2003
Earlier work this paper cites.
Closed-Form Factorization of Latent Semantics in GANs
Shen, Y.; and Zhou, B. 2020 · 2007
Earlier work this paper cites.
ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
Covariate balancing propensity score
Imai, K.; and Ratkovic, M. 2014 · 2014
Earlier work this paper cites.
Training Very Deep Networks
Srivastava, R. K.; Greff, K.; and Schmidhuber, J. 2015 · 2015
Earlier work this paper cites.
Infogan: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Chen, X.; Duan, Y.; Houthooft, R.; Schulman, J.; Sutskever, I.; and Abbeel, P. 2016 · 2016
Earlier work this paper cites.
Wide & Deep Learning for Recommender Systems
Cheng, H.-T.; Koc, L.; Harmsen, J.; Shaked, T.; Chandra, T.; Aradhye, H.; Anderson, G.; Corrado, G.; Chai, W.; Ispir, M.; Anil, R.; Haque, Z.; Hong, L.; Jain, V.; Liu, X.; and Shah, H. 2016 · 2016
Earlier work this paper cites.
Domain-Adversarial Training of Neural Networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J.; Divvala, S. K.; Girshick, R. B.; and Farhadi, A. 2016 · 2016
Cited alongside, same era.
Improved Regularization of Convolutional Neural Networks with Cutout
DeVries, T.; and Taylor, G. W. 2017 · 2017
Cited alongside, same era.
Deep Pyramidal Residual Networks
Han, D.; Kim, J.; and Kim, J. 2017 · 2017
Cited alongside, same era.
Low-shot Visual Recognition by Shrinking and Hallucinating Features
Hariharan, B.; and Girshick, R. 2017 · 2017
Cited alongside, same era.
β \beta -vae: Learning basic visual concepts with a constrained variational framework
Higgins, I.; Matthey, L.; Pal, A.; Burgess, C.; Glorot, X.; Botvinick, M.; Mohamed, S.; and Lerchner, A. 2017 · 2017
Cited alongside, same era.
Deep Feature Interpolation for Image Content Changes
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Later among the works it cites.
Domain Generalization via Model-Agnostic Learning of Semantic Features
Dou, Q.; Castro, D. C.; Kamnitsas, K.; and Glocker, B. 2019 · 2019
Later among the works it cites.
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Hendrycks, D.; Mu, N.; Cubuk, E. D.; Zoph, B.; Gilmer, J.; and Lakshminarayanan, B. 2019 · 2019
Later among the works it cites.
Disentangled Graph Convolutional Networks
Ma, J.; Cui, P.; Kuang, K.; Wang, X.; and Zhu, W. 2019 · 2019
Later among the works it cites.
Domain Agnostic Learning with Disentangled Representations
Peng, X.; Huang, Z.; Sun, X.; and Saenko, K. 2019 · 2019
Later among the works it cites.
Implicit Semantic Data Augmentation for Deep Networks
Wang, Y.; Pan, X.; Song, S.; Zhang, H.; Huang, G.; and Wu, C. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Upchurch, P.; Gardner, J.; Pleiss, G.; Pless, R.; Snavely, N.; Bala, K.; and Weinberger, K. 2017 · 2017
Cited alongside, same era.
mixup: Beyond Empirical Risk Minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2017 · 2017
Cited alongside, same era.
Sanity Checks for Saliency Maps
Adebayo, J.; Gilmer, J.; Muelly, M.; Goodfellow, I.; Hardt, M.; and Kim, B. 2018 · 2018
Cited alongside, same era.
Stable Prediction across Unknown Environments
Kuang, K.; Cui, P.; Athey, S.; Xiong, R.; and Li, B. 2018 · 2018
Cited alongside, same era.
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
Liu, Y.-C.; Yeh, Y.-Y.; Fu, T.-C.; Wang, S.-D.; Chiu, W.-C.; and Frank Wang, Y.-C. 2018 · 2018
Cited alongside, same era.
Generalizing Across Domains via Cross-Gradient Training
Shankar, S.; Piratla, V.; Chakrabarti, S.; Chaudhuri, S.; Jyothi, P.; and Sarawagi, S. 2018 · 2018
Cited alongside, same era.
Domain Generalization by Solving Jigsaw Puzzles
Carlucci, F. M.; D’Innocente, A.; Bucci, S.; Caputo, B.; and Tommasi, T. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Cutmix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. 2019 · 2019
Later among the works it cites.
Learning De-biased Representations with Biased Representations
Bahng, H.; Chun, S.; Yun, S.; Choo, J.; and Oh, S. J. 2020 · 2020
Closest in time.
Towards Non-IID Image Classification: A Dataset and Baselines
He, Y.; Shen, Z.; and Cui, P. 2020 · 2020
Closest in time.
Towards Recognizing Unseen Categories in Unseen Domains
Mancini, M.; Akata, Z.; Ricci, E.; and Caputo, B. 2020 · 2020
Closest in time.
Stable Learning via Sample Reweighting
Shen, Z.; Cui, P.; Zhang, T.; and Kuang, K. 2020 · 2020
Closest in time.