Fetching the paper…
Reading the bibliography…
Generative Adversarial Networks (GANs) have been shown to produce realistically looking synthetic images with remarkable success, yet their performance seems less impressive when the training set is highly diverse.
Multi-scale structural similarity for image quality assessment
Zhou Wang, Eero Simoncelli, Alan Bovik, et al · 2003
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Earlier work this paper cites.
An analysis of single layer networks in unsupervised feature learning
Andrew Y. Ng Adam Coates, Honglak Lee · 2011
Earlier work this paper cites.
Auto-Encoding Variational Bayes
D. P Kingma and M. Welling · 2013
Earlier work this paper cites.
Evaluation of traffic sign recognition methods trained on synthetically generated data
Boris Moiseev, Artem Konev, Alexander Chigorin, and Anton Konushin · 2013
Earlier work this paper cites.
Generative Adversarial Networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Semi-Supervised Learning with Deep Generative Models
D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling · 2014
Earlier work this paper cites.
Conditional Generative Adversarial Nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
A. Boesen Lindbo Larsen, S. Kaae Sønderby, H. Larochelle, and O. Winther · 2015
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
Earlier work this paper cites.
Adversarial Autoencoders
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey · 2015
Earlier work this paper cites.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
A. Radford, L. Metz, and S. Chintala · 2015
Earlier work this paper cites.
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
J. T. Springenberg · 2015
Earlier work this paper cites.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
Cited alongside, same era.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Cited alongside, same era.
Image-to-Image Translation with Conditional Adversarial Networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2016
Cited alongside, same era.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2016
Cited alongside, same era.
Least Squares Generative Adversarial Networks
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley · 2016
Cited alongside, same era.
Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization
Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng, Weidong Cai, and Heng Huang · 2017
Later among the works it cites.
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
H.-W. Dong, W.-Y. Hsiao, L.-C. Yang, and Y.-H. Yang · 2017
Later among the works it cites.
Improved Training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
Later among the works it cites.
Deep clustering with convolutional autoencoders
Xifeng Guo, Xinwang Liu, En Zhu, and Jianping Yin · 2017
Later among the works it cites.
Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Conditional Image Synthesis With Auxiliary Classifier GANs
A. Odena, C. Olah, and J. Shlens · 2016
Cited alongside, same era.
Generative Adversarial Text to Image Synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
Cited alongside, same era.
Improved Techniques for Training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross Girshick, and Ali Farhadi · 2016
Cited alongside, same era.
Semantic Image Inpainting with Deep Generative Models
R. A. Yeh, C. Chen, T. Yian Lim, A. G. Schwing, M. Hasegawa-Johnson, and M. N. Do · 2016
Cited alongside, same era.
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
L. Yu, W. Zhang, J. Wang, and Y. Yu · 2016
Cited alongside, same era.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
T. Kim, M. Cha, H. Kim, J. K. Lee, and J. Kim · 2017
Later among the works it cites.
SEGAN: Speech Enhancement Generative Adversarial Network
S. Pascual, A. Bonafonte, and J. Serrà · 2017
Later among the works it cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Later among the works it cites.
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
L.-C. Yang, S.-Y. Chou, and Y.-H. Yang · 2017
Later among the works it cites.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Later among the works it cites.
Clustering and unsupervised anomaly detection with l2 normalized deep auto-encoder representations
Caglar Aytekin, Xingyang Ni, Francesco Cricri, and Emre Aksu · 2018
Closest in time.
Ozsel Kilinc and Ismail Uysal · 2018
Closest in time.
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
D. Weinshall, G. Cohen, and D. Amir · 2018
Closest in time.