50 - Deep Learning - Plain Version 2020/ClipID:21184 vorhergehender Clip nächster Clip

Die automatischen Untertitel, die mit Whisper Open AI in diesem Video-Player (und im Multistream-Video-Player) generiert werden, dienen der Bequemlichkeit und Barrierefreiheit. Es ist jedoch zu beachten, dass die Genauigkeit und Interpretation variieren können. Für mehr Informationen lesen Sie bitte die FAQs (Absatz 14)
Aufnahme Datum 2020-10-12

Kurs-Verknüpfung

Deep Learning - Plain Version

Zugang

Frei

Sprache

Englisch

Einrichtung

Lehrstuhl für Informatik 5 (Mustererkennung)

Produzent

Lehrstuhl für Informatik 5 (Mustererkennung)

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.

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.

Nächstes Video

Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei
Maier, Andreas
Prof. Dr. Andreas Maier
2020-10-12
Frei

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