Lecture Notes in Deep Learning: Weakly and Self-supervised Learning – Part 2

Symbolic picture for the article. The link opens the image in a large view.

From 2-D to 3-D Annotations

These are the lecture notes for FAU’s YouTube Lecture “Deep Learning“. This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. Try it yourself! If you spot mistakes, please let us know!

In the wild data can also be employed for training weakly-supervised 3D systems. Image created using gifify. Source: YouTube.

Welcome back to deep learning! So today, we want to continue talking about weekly annotating examples. Today’s topic will be particularly focusing on 3-D annotations.

Let’s look that those dense %$!”%$$!!! Image under CC BY 4.0 from the Deep Learning Lecture.

So welcome back to our lectures on weakly and self-supervised learning, the second part: From sparse annotations to dense segmentations. Now, what means dense, here? “You dense …”. Probably not.

Can we extend 2-D annotations to 3-D? Image under CC BY 4.0 from the Deep Learning Lecture.

What we are actually interested in is a dense 3-D segmentation. Here, you have an example of a volumetric image and you can see that we have a couple of slices that we visualize on the left-hand side. Then, we can annotate each of these slices, and then we can use them for training, for example, of a 3-D U-net to produce a full 3-D segmentation. As you already have might have guessed annotating all of the slices subsequently probably with different orientations in order to get at rid of bias that is introduced by the slice orientation is extremely tedious. So, you don’t want to do that. What we will look into in the next couple of minutes is talking about how to use sparsely sampled slices in order to get a full automatic 3-D segmentation. Also, this approach is interesting because it allows for interactive correction.

Let’s exploit the properties of one-hot-encoded labels in 3-D. Image under CC BY 4.0 from the Deep Learning Lecture.

Let’s look into this idea. You train with sparse labels. Typically, we have these one-hot labels, essentially being one if you are part of the segmentation mask. The mask is either true or false. Then, you get this cross-entropy loss where you essentially then backpropagate with. You use exactly the label that returned one because it was annotated. But, of course, that’s not true for the non annotated samples. What you can do is you can develop this further to something that is called a weighted cross-entropy loss. Here, you multiply the original cross-entropy with an additional weight w. w is set in a way that it’s zero if it’s not labeled. You can assign a weight that’s greater than zero otherwise. By the way, if you have this w, you can also extend it to be interactive by updating y and the w(y). So, this means that you update the labels over the iterations with users. If you do so then, you can actually work with sparsely annotated 3-D volumes and train algorithms to produce complete 3-D segmentations.

Take away messages for weak supervision. Image under CC BY 4.0 from the Deep Learning Lecture.

Let’s look at some takeaway messages. Weakly supervised learning is actually an approach to omit fine-grained labels because they’re expensive. We try to get away with something that is much cheaper. The core idea is that the label has less detail than the target and the methods essentially depend on prior knowledge, like knowledge about the object, knowledge about the distribution, or even a prior algorithm that can help you with the refinement of the labels, and weak labels that we called hints earlier.

We can also use audio cues for weak supervision. Image created using gifify. Source: YouTube.

Typically this is inferior to fully supervised training, but it’s highly relevant in practice because annotations are very very costly. Don’t forget about transfer learning. This can also help you. We discussed this already quite a bit in earlier lectures. What we’ve seen here is, of course, related to semi-supervised learning and self-supervised learning.

More exciting things coming up in this deep learning lecture. Image under CC BY 4.0 from the Deep Learning Lecture.

This is also the reason why we talk next time about the topic of self-supervision and how these ideas have sparked quite some boost in the field over the last couple of years. So, thank you very much for listening and looking forward to seeing you in the next video. Bye-bye!

However, audio may be ambiguous at occasions. Image created using gifify. Source: YouTube.

If you liked this post, you can find more essays here, more educational material on Machine Learning here, or have a look at our Deep LearningLecture. I would also appreciate a follow on YouTubeTwitterFacebook, or LinkedIn in case you want to be informed about more essays, videos, and research in the future. This article is released under the Creative Commons 4.0 Attribution License and can be reprinted and modified if referenced. If you are interested in generating transcripts from video lectures try AutoBlog.


[1] Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, et al. “3d u-net: learning dense volumetric segmentation from sparse annotation”. In: MICCAI. Springer. 2016, pp. 424–432.
[2] Waleed Abdulla. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. Accessed: 27.01.2020. 2017.
[3] Olga Russakovsky, Amy L. Bearman, Vittorio Ferrari, et al. “What’s the point: Semantic segmentation with point supervision”. In: CoRR abs/1506.02106 (2015). arXiv: 1506.02106.
[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.
[6] Anna Khoreva, Rodrigo Benenson, Jan Hosang, et al. “Simple Does It: Weakly Supervised Instance and Semantic Segmentation”. In: arXiv preprint arXiv:1603.07485 (2016).
[7] Kaiming He, Georgia Gkioxari, Piotr Dollár, et al. “Mask R-CNN”. In: CoRR abs/1703.06870 (2017). arXiv: 1703.06870.
[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.
[10] Alexander Kolesnikov and Christoph H. Lampert. “Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation”. In: CoRR abs/1603.06098 (2016). arXiv: 1603.06098.
[11] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, et al. “Microsoft COCO: Common Objects in Context”. In: CoRR abs/1405.0312 (2014). arXiv: 1405.0312.
[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.
[13] K. Simonyan, A. Vedaldi, and A. Zisserman. “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps”. In: Proc. ICLR (workshop track). 2014.
[14] Bolei Zhou, Aditya Khosla, Agata Lapedriza, et al. “Learning deep features for discriminative localization”. In: Proc. CVPR. 2016, pp. 2921–2929.
[15] Longlong Jing and Yingli Tian. “Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey”. In: arXiv e-prints, arXiv:1902.06162 (Feb. 2019). arXiv: 1902.06162 [cs.CV].
[16] D. Pathak, P. Krähenbühl, J. Donahue, et al. “Context Encoders: Feature Learning by Inpainting”. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016, pp. 2536–2544.
[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.
[20] Mathilde Caron, Piotr Bojanowski, Armand Joulin, et al. “Deep Clustering for Unsupervised Learning of Visual Features”. In: Computer Vision – ECCV 2018. Cham: Springer International Publishing, 2018, pp. 139–156. A.
[21] A. Dosovitskiy, P. Fischer, J. T. Springenberg, et al. “Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks”. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 38.9 (Sept. 2016), pp. 1734–1747.
[22] V. Christlein, M. Gropp, S. Fiel, et al. “Unsupervised Feature Learning for Writer Identification and Writer Retrieval”. In: 2017 14th IAPR International Conference on Document Analysis and Recognition Vol. 01. Nov. 2017, pp. 991–997.
[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].
33] Prannay Khosla, Piotr Teterwak, Chen Wang, et al. “Supervised Contrastive Learning”. In: arXiv e-prints, arXiv:2004.11362 (Apr. 2020). arXiv: 2004.11362 [cs.LG].
[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].