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Recording date 2020-10-12

Course

Deep Learning

Language

English

Organisational Unit

Friedrich-Alexander-Universität Erlangen-Nürnberg

Producer

Friedrich-Alexander-Universität Erlangen-Nürnberg

Deep Learning - Unsupervised Learning Part 5

In this last video on unsupervised learning, we introduce some more advanced GAN concepts to avoid mode collapse and strong intra-batch correlation using virtual batch normalization, unrolled GANs, and minibatch discrimination.

For reminders to watch the new video follow on Twitter or LinkedIn.

Video References:
X-ray GAN
Anime GAN
PortraitGAN
BigGAN

Further Reading:
A gentle Introduction to Deep Learning

Links
Link - Variational Autoencoders:
Link - NIPS 2016 GAN Tutorial of Goodfellow
Link - How to train a GAN? Tips and tricks to make GANs work (careful, not
everything is true anymore!)
Link - Ever wondered about how to name your GAN?

References
[1] Xi Chen, Xi Chen, Yan Duan, et al. “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”. In: Advances in Neural Information Processing Systems 29. Curran Associates, Inc., 2016, pp. 2172–2180.
[2] Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, et al. “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion”. In: Journal of Machine Learning Research 11.Dec (2010), pp. 3371–3408.
[3] Emily L. Denton, Soumith Chintala, Arthur Szlam, et al. “Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks”. In: CoRR abs/1506.05751 (2015). arXiv: 1506.05751.
[4] Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern classification. 2nd ed. New York: Wiley-Interscience, Nov. 2000.
[5] Asja Fischer and Christian Igel. “Training restricted Boltzmann machines: An introduction”. In: Pattern Recognition 47.1 (2014), pp. 25–39.
[6] John Gauthier. Conditional generative adversarial networks for face generation. Mar. 17, 2015. URL: http://www.foldl.me/2015/conditional-gans-face-generation/ (visited on 01/22/2018).
[7] Ian Goodfellow. NIPS 2016 Tutorial: Generative Adversarial Networks. 2016. eprint: arXiv:1701.00160.
[8] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, et al. “GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium”. In: Advances in Neural Information Processing Systems 30. Curran Associates, Inc., 2017, pp. 6626–6637.
[9] Geoffrey E Hinton and Ruslan R Salakhutdinov. “Reducing the dimensionality of data with neural networks.” In: Science 313.5786 (July 2006), pp. 504–507. arXiv: 20.
[10] Geoffrey E. Hinton. “A Practical Guide to Training Restricted Boltzmann Machines”. In: Neural Networks: Tricks of the Trade: Second Edition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 599–619.
[11] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, et al. “Image-to-Image Translation with Conditional Adversarial Networks”. In: (2016). eprint: arXiv:1611.07004.
[12] Diederik P Kingma and Max Welling. “Auto-Encoding Variational Bayes”. In: arXiv e-prints, arXiv:1312.6114 (Dec. 2013), arXiv:1312.6114. arXiv: 1312.6114 [stat.ML].
[13] Jonathan Masci, Ueli Meier, Dan Ciresan, et al. “Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction”. In: Artificial Neural Networks and Machine Learning – ICANN 2011. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 52–59.
[14] Luke Metz, Ben Poole, David Pfau, et al. “Unrolled Generative Adversarial Networks”. In: International Conference on Learning Representations. Apr. 2017. eprint: arXiv:1611.02163.
[15] Mehdi Mirza and Simon Osindero. “Conditional Generative Adversarial Nets”. In: CoRR abs/1411.1784 (2014). arXiv: 1411.1784.
[16] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial 2015. eprint: arXiv:1511.06434.
[17] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, et al. “Improved Techniques for Training GANs”. In: Advances in Neural Information Processing Systems 29. Curran Associates, Inc., 2016, pp. 2234–2242.
[18] Andrew Ng. “CS294A Lecture notes”. In: 2011.
[19] Han Zhang, Tao Xu, Hongsheng Li, et al. “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks”. In: CoRR abs/1612.03242 (2016). arXiv: 1612.03242.
[20] Han Zhang, Tao Xu, Hongsheng Li, et al. “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks”. In: arXiv preprint arXiv:1612.03242 (2016).
[21] Bolei Zhou, Aditya Khosla, Agata Lapedriza, et al. “Learning Deep Features for Discriminative Localization”. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, June 2016, pp. 2921–2929. arXiv: 1512.04150.
[22] Jun-Yan Zhu, Taesung Park, Phillip Isola, et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”. In: CoRR abs/1703.10593 (2017). arXiv: 1703.10593.

Up next

Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
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Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
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Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
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Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
IdM-login
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
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