Built on

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Colson, B., Marcotte, P., Savard, G.: An overview of bilevel optimization. Annals of operations research 153
2007
Earlier work this paper cites.
Nasrabadi, N.M.: Pattern recognition and machine learning. Journal of electronic imaging 16
2007
Earlier work this paper cites.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. rep., Citeseer (2009)
2009
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Huszár, F.: How (not) to train your generative model: Scheduled sampling, likelihood, adversary? arXiv preprint arXiv:1511.05101 (2015)
Original
2015
Earlier work this paper cites.
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300 (2015)
Original
2015
Earlier work this paper cites.

Similar

Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: International Conference on Machine Learning. pp. 1718–1727 (2015)
2015
Cited alongside, same era.
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (December 2015)
2015
Cited alongside, same era.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Original
2015
Cited alongside, same era.
Theis, L., Oord, A.v.d., Bethge, M.: A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844 (2015)
Original
2015
Cited alongside, same era.
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., Courville, A.: Adversarially learned inference. arXiv preprint arXiv:1606.00704 (2016)

Then

Oord, A.v.d., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759 (2016)
Original
2016
Later among the works it cites.
Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017)
Original
2017
Later among the works it cites.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning. pp. 214–223 (2017)
2017
Later among the works it cites.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
Original
2017
Later among the works it cites.
Lin, Z., Khetan, A., Fanti, G., Oh, S.: Pacgan: The power of two samples in generative adversarial networks. arXiv preprint arXiv:1712.04086 (2017)

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2018

Learning Implicit Generative Models by Teaching Explicit Ones

Du, Chao, Xu, Kun, Li, Chongxuan et al.

Understand

Implicit generative models are difficult to train as no explicit density functions are defined.

Generative adversarial nets (GANs) present a minimax framework to train such models, which however can suffer from mode collapse due to the nature of the JS-divergence.
This paper presents a learning by teaching (LBT) approach to learning implicit models, which intrinsically avoids the mode collapse problem by optimizing a KL-divergence rather than the JS-divergence in GANs.
In LBT, an auxiliary density estimator is introduced to fit the implicit model's distribution while the implicit model teaches the density estimator to match the data distribution.

Built on

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Colson, B., Marcotte, P., Savard, G.: An overview of bilevel optimization. Annals of operations research 153
2007
Earlier work this paper cites.
Nasrabadi, N.M.: Pattern recognition and machine learning. Journal of electronic imaging 16
2007
Earlier work this paper cites.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. rep., Citeseer (2009)
2009
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Huszár, F.: How (not) to train your generative model: Scheduled sampling, likelihood, adversary? arXiv preprint arXiv:1511.05101 (2015)
Original
2015
Earlier work this paper cites.
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300 (2015)
Original
2015
Earlier work this paper cites.

Similar

Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: International Conference on Machine Learning. pp. 1718–1727 (2015)
2015
Cited alongside, same era.
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (December 2015)
2015
Cited alongside, same era.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Original
2015
Cited alongside, same era.
Theis, L., Oord, A.v.d., Bethge, M.: A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844 (2015)
Original
2015
Cited alongside, same era.
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., Courville, A.: Adversarially learned inference. arXiv preprint arXiv:1606.00704 (2016)

Then

Oord, A.v.d., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759 (2016)
Original
2016
Later among the works it cites.
Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017)
Original
2017
Later among the works it cites.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning. pp. 214–223 (2017)
2017
Later among the works it cites.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
Original
2017
Later among the works it cites.
Lin, Z., Khetan, A., Fanti, G., Oh, S.: Pacgan: The power of two samples in generative adversarial networks. arXiv preprint arXiv:1712.04086 (2017)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2016

Cited alongside, same era.

Goodfellow, I.: Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160 (2016)

Original

2016

Cited alongside, same era.

Metz, L., Poole, B., Pfau, D., Sohl-Dickstein, J.: Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163 (2016)

Original

2016

Cited alongside, same era.

Mohamed, S., Lakshminarayanan, B.: Learning in implicit generative models. arXiv preprint arXiv:1610.03483 (2016)

Original

2016

Cited alongside, same era.

Nowozin, S., Cseke, B., Tomioka, R.: f-gan: Training generative neural samplers using variational divergence minimization. In: Advances in Neural Information Processing Systems. pp. 271–279 (2016)

2016

Cited alongside, same era.

Original

2017