Fetching the paper…
Reading the bibliography…
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution.
On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 1905
Earlier work this paper cites.
On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 1905
Earlier work this paper cites.
No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
Earlier work this paper cites.
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.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
Xuanlong Nguyen, Martin J Wainwright, and Michael I Jordan · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Testing the manifold hypothesis
Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Cited alongside, same era.
Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Cited alongside, same era.
Generative adversarial positive-unlabeled learning
Ming Hou, Brahim Chaib-Draa, Chao Li, and Qibin Zhao · 2018
Later among the works it cites.
Unsupervised adversarial invariance
Ayush Jaiswal, Rex Yue Wu, Wael Abd-Almageed, and Prem Natarajan · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Later among the works it cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Later among the works it cites.
Bias and generalization in deep generative models: An empirical study
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, and Stefano Ermon · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Adversarial contrastive estimation
Avishek Joey Bose, Huan Ling, and Yanshuai Cao · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
Cited alongside, same era.
Actively avoiding nonsense in generative models
Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, and Christos Tzamos · 2018
Cited alongside, same era.
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2018
Later among the works it cites.
A critical analysis of self-supervision, or what we can learn from a single image
Yuki M Asano, Christian Rupprecht, and Andrea Vedaldi · 2019
Later among the works it cites.
Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
Later among the works it cites.
Video representation learning by dense predictive coding
Tengda Han, Weidi Xie, and Andrew Zisserman · 2019
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
Later among the works it cites.
Understanding the limitations of variational mutual information estimators
Jiaming Song and Stefano Ermon · 2019
Later among the works it cites.
Difference-seeking generative adversarial network–unseen sample generation
Yi Lin Sung, Sung-Hsien Hsieh, Soo-Chang Pei, and Chun-Shien Lu · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Later among the works it cites.