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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 - Weakly and Self-Supervised Learning Part 4

In this video, we look into contrastive losses and how they can be used in combination with self-supervised learning.

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Further Reading:
A gentle Introduction to Deep Learning

References
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[4] Marius Cordts, Mohamed Omran, Sebastian Ramos, et al. “The Cityscapes Dataset for Semantic Urban Scene Understanding”. In: CoRR abs/1604.01685 (2016). arXiv: 1604.01685.
[5] Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern classification. 2nd ed. New York: Wiley-Interscience, Nov. 2000.
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[9] Maxime Oquab, Léon Bottou, Ivan Laptev, et al. “Is object localization for free? weakly-supervised learning with convolutional neural networks”. In: Proc. CVPR. 2015, pp. 685–694.
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[17] C. Doersch, A. Gupta, and A. A. Efros. “Unsupervised Visual Representation Learning by Context Prediction”. In: 2015 IEEE International Conference on Computer Vision (ICCV). Dec. 2015, pp. 1422–1430.
[18] Mehdi Noroozi and Paolo Favaro. “Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles”. In: Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016, pp. 69–84.
[19] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. “Unsupervised Representation Learning by Predicting Image Rotations”. In: International Conference on Learning Representations. 2018.
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[23] Z. Ren and Y. J. Lee. “Cross-Domain Self-Supervised Multi-task Feature Learning Using Synthetic Imagery”. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 2018, pp. 762–771.
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[25] Ben Poole, Sherjil Ozair, Aaron Van Den Oord, et al. “On Variational Bounds of Mutual Information”. In: Proceedings of the 36th International Conference on Machine Learning. Vol. 97. Proceedings of Machine Learning Research. Long Beach, California, USA: PMLR, Sept. 2019, pp. 5171–5180.
[26] R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, et al. “Learning deep representations by mutual information estimation and maximization”. In: International Conference on Learning Representations. 2019.
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[29] Yonglong Tian, Dilip Krishnan, and Phillip Isola. “Contrastive Multiview Coding”. In: arXiv e-prints, arXiv:1906.05849 (June 2019), arXiv:1906.05849. arXiv: 1906.05849 [cs.CV].
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[31] Ting Chen, Simon Kornblith, Mohammad Norouzi, et al. “A Simple Framework for Contrastive Learning of Visual Representations”. In: arXiv e-prints, arXiv:2002.05709 (Feb. 2020), arXiv:2002.05709. arXiv: 2002.05709 [cs.LG].
[32] Ishan Misra and Laurens van der Maaten. “Self-Supervised Learning of Pretext-Invariant Representations”. In: arXiv e-prints, arXiv:1912.01991 (Dec. 2019). arXiv: 1912.01991 [cs.CV].
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[34] Jean-Bastien Grill, Florian Strub, Florent Altché, et al. “Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning”. In: arXiv e-prints, arXiv:2006.07733 (June 2020), arXiv:2006.07733. arXiv: 2006.07733 [cs.LG].
[35] Tongzhou Wang and Phillip Isola. “Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere”. In: arXiv e-prints, arXiv:2005.10242 (May 2020), arXiv:2005.10242. arXiv: 2005.10242 [cs.LG].
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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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