Fetching the paper…
Reading the bibliography…
Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i.i.d.
The need for biases in learning generalizations
T. M. Mitchell · 1980
Earlier work this paper cites.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2004
Earlier work this paper cites.
Labelme: a database and web-based tool for image annotation
B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Exploiting hierarchical context on a large database of object categories
M. J. Choi, J. J. Lim, A. Torralba, and A. S. Willsky · 2010
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
C. Fang, Y. Xu, and D. N. Rockmore · 2013
Earlier work this paper cites.
Domain generalization via invariant feature representation
K. Muandet, D. Balduzzi, and B. Schölkopf · 2013
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2014
Earlier work this paper cites.
The cifar-10 dataset
A. Krizhevsky, V. Nair, and G. Hinton · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2016
Cited alongside, same era.
Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. Belongie · 2017
Cited alongside, same era.
Measuring the tendency of cnns to learn surface statistical regularities
J. Jo and Y. Bengio · 2017
Cited alongside, same era.
Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales · 2017
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
R. Volpi, H. Namkoong, O. Sener, J. C. Duchi, V. Murino, and S. Savarese · 2018
Later among the works it cites.
One-shot imitation from observing humans via domain-adaptive meta-learning
T. Yu, C. Finn, A. Xie, S. Dasari, T. Zhang, P. Abbeel, and S. Levine · 2018
Later among the works it cites.
Domain generalization by solving jigsaw puzzles
F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi · 2019
Later among the works it cites.
Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation
J. Choi, T. Kim, and C. Kim · 2019
Later among the works it cites.
Domain generalization via model-agnostic learning of semantic features
Q. Dou, D. C. de Castro, K. Kamnitsas, and B. Glocker · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Cited alongside, same era.
Deep hashing network for unsupervised domain adaptation
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan · 2017
Cited alongside, same era.
Metareg: Towards domain generalization using meta-regularization
Y. Balaji, S. Sankaranarayanan, and R. Chellappa · 2018
Cited alongside, same era.
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2018
Cited alongside, same era.
Auggan: Cross domain adaptation with gan-based data augmentation
S.-W. Huang, C.-T. Lin, S.-P. Chen, Y.-Y. Wu, P.-H. Hsu, and S.-H. Lai · 2018
Cited alongside, same era.
A dirt-t approach to unsupervised domain adaptation
R. Shu, H. H. Bui, H. Narui, and S. Ermon · 2018
Cited alongside, same era.
Domain generalization with domain-specific aggregation modules
A. D’Innocente and B. Caputo · 2019
Later among the works it cites.
Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
Later among the works it cites.
Exploring the origins and prevalence of texture bias in convolutional neural networks
K. L. Hermann and S. Kornblith · 2019
Later among the works it cites.
Episodic training for domain generalization
D. Li, J. Zhang, Y. Yang, C. Liu, Y.-Z. Song, and T. M. Hospedales · 2019
Later among the works it cites.
Domain generalization using a mixture of multiple latent domains
T. Matsuura and T. Harada · 2019
Later among the works it cites.
Do imagenet classifiers generalize to imagenet?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
Later among the works it cites.
Learning robust global representations by penalizing local predictive power
H. Wang, S. Ge, Z. Lipton, and E. P. Xing · 2019
Later among the works it cites.