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Recording date 2020-10-12

Via

Free

Language

English

Organisational Unit

Lehrstuhl für Informatik 5 (Mustererkennung)

Producer

Lehrstuhl für Informatik 5 (Mustererkennung)

Format

lecture

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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[2] Waleed Abdulla. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. Accessed: 27.01.2020. 2017.
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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.
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[8] Sangheum Hwang and Hyo-Eun Kim. “Self-Transfer Learning for Weakly Supervised Lesion Localization”. In: MICCAI. Springer. 2016, pp. 239–246.
[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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[12] Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, et al. “Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization”. In: CoRR abs/1610.02391 (2016). arXiv: 1610.02391.
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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.
[24] Asano YM., Rupprecht C., and Vedaldi A. “Self-labelling via simultaneous clustering and representation learning”. In: International Conference on Learning Representations. 2020.
[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.
[27] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. “Representation Learning with Contrastive Predictive Coding”. In: arXiv e-prints, arXiv:1807.03748 (July 2018). arXiv: 1807.03748 [cs.LG].
[28] Philip Bachman, R Devon Hjelm, and William Buchwalter. “Learning Representations by Maximizing Mutual Information Across Views”. In: Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 2019, pp. 15535–15545.
[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].
[30] Kaiming He, Haoqi Fan, Yuxin Wu, et al. “Momentum Contrast for Unsupervised Visual Representation Learning”. In: arXiv e-prints, arXiv:1911.05722 (Nov. 2019). arXiv: 1911.05722 [cs.CV].
[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].
[36] Junnan Li, Pan Zhou, Caiming Xiong, et al. “Prototypical Contrastive Learning of Unsupervised Representations”. In: arXiv e-prints, arXiv:2005.04966 (May 2020), arXiv:2005.04966. arXiv: 2005.04966 [cs.CV].

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