Fetching the paper…
Reading the bibliography…
This paper considers the general $f$-divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases.
T. Zhang, “Statistical behavior and consistency of classification methods based on convex risk minimization,” Annals of Statistics
2004
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in ICLR
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems
2014
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al
2015
Earlier work this paper cites.
S. Nowozin, B. Cseke, and R. Tomioka, “f-gan: Training generative neural samplers using variational divergence minimization,” in Advances in neural information processing systems
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improved variational inference with inverse autoregressive flow,” in Advances in neural information processing systems
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” in ICML
2016
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial autoencoders,” in ICLR
2016
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” in ICLR
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision
2016
Cited alongside, same era.
2016
Cited alongside, same era.
J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” in ICLR
2017
Cited alongside, same era.
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. C. Courville, “Adversarially learned inference,” in ICLR
2017
Cited alongside, same era.
L. Mescheder, S. Nowozin, and A. Geiger, “Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70
I. O. Tolstikhin, O. Bousquet, S. Gelly, and B. Schölkopf, “Wasserstein auto-encoders,” in ICLR
2018
Later among the works it cites.
H. Kim and A. Mnih, “Disentangling by factorising,” in ICML
2018
Later among the works it cites.
Z. Lin, A. Khetan, G. Fanti, and S. Oh, “Pacgan: The power of two samples in generative adversarial networks,” in Advances in Neural Information Processing Systems
2018
Later among the works it cites.
Y. Li and R. E. Turner, “Gradient estimators for implicit models,” in ICLR
2018
Later among the works it cites.
J. Shi, S. Sun, and J. Zhu, “A spectral approach to gradient estimation for implicit distributions,” in ICML
2018
Later among the works it cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in ICLR
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
A. Srivastava, L. Valkov, C. Russell, M. U. Gutmann, and C. Sutton, “Veegan: Reducing mode collapse in gans using implicit variational learning,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision
2017
Cited alongside, same era.
C. Li, H. Liu, C. Chen, Y. Pu, L. Chen, R. Henao, and L. Carin, “Alice: Towards understanding adversarial learning for joint distribution matching,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
F. Huszár, “Variational inference using implicit distributions,” arXiv preprint arXiv:1702.08235
2017
Cited alongside, same era.
Y. Wu, Y. Burda, R. Salakhutdinov, and R. Grosse, “On the quantitative analysis of decoder-based generative models,” in ICLR
2017
Cited alongside, same era.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
2018
Later among the works it cites.
J. Donahue and K. Simonyan, “Large scale adversarial representation learning,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
R. Johnson and T. Zhang, “A framework of composite functional gradient methods for generative adversarial models.,” IEEE transactions on pattern analysis and machine intelligence
2019
Later among the works it cites.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in ICML
2019
Later among the works it cites.
A. Brock, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” in ICLR
2019
Later among the works it cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2019
Later among the works it cites.
L. Wen, Y. Zhou, L. He, M. Zhou, and Z. Xu, “Mutual information gradient estimation for representation learning,” in ICLR
2020
Closest in time.