5 - Deep Learning - Plain Version 2020/ClipID:20840 previous clip next clip

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Recording date 2020-10-04

Via

Free

Language

English

Organisational Unit

Lehrstuhl für Informatik 5 (Mustererkennung)

Producer

Lehrstuhl für Informatik 5 (Mustererkennung)

Format

lecture

Deep Learning - Introduction Part 5
 
This video introduces the topic of Deep Learning and presents the course's requirements, grading procedures, and summarises the first unit.
 
For reminders to watch the new video follow on Twitter or LinkedIn.
 
References
[1] David Silver, Julian Schrittwieser, Karen Simonyan, et al. “Mastering the game of go without human knowledge”. In: Nature 550.7676 (2017), p. 354.
[2] David Silver, Thomas Hubert, Julian Schrittwieser, et al. “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm”. In: arXiv preprint arXiv:1712.01815 (2017).
[3] M. Aubreville, M. Krappmann, C. Bertram, et al. “A Guided Spatial Transformer Network for Histology Cell Differentiation”. In: ArXiv e-prints (July 2017). arXiv: 1707.08525 [cs.CV].
[4] David Bernecker, Christian Riess, Elli Angelopoulou, et al. “Continuous short-term irradiance forecasts using sky images”. In: Solar Energy 110 (2014), pp. 303–315.
[5] Patrick Ferdinand Christ, Mohamed Ezzeldin A Elshaer, Florian Ettlinger, et al. “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields”. In: International Conference on Medical Image Computing and Computer-Assisted Springer. 2016, pp. 415–423.
[6] Vincent Christlein, David Bernecker, Florian Hönig, et al. “Writer Identification Using GMM Supervectors and Exemplar-SVMs”. In: Pattern Recognition 63 (2017), pp. 258–267.
[7] Florin Cristian Ghesu, Bogdan Georgescu, Tommaso Mansi, et al. “An Artificial Agent for Anatomical Landmark Detection in Medical Images”. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Athens, 2016, pp. 229–237.
[8] Jia Deng, Wei Dong, Richard Socher, et al. “Imagenet: A large-scale hierarchical image database”. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference IEEE. 2009, pp. 248–255.
[9] A. Karpathy and L. Fei-Fei. “Deep Visual-Semantic Alignments for Generating Image Descriptions”. In: ArXiv e-prints (Dec. 2014). arXiv: 1412.2306 [cs.CV].
[10] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. “ImageNet Classification with Deep Convolutional Neural Networks”. In: Advances in Neural Information Processing Systems 25. Curran Associates, Inc., 2012, pp. 1097–1105.
[11] Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, et al. “You Only Look Once: Unified, Real-Time Object Detection”. In: CoRR abs/1506.02640 (2015).
[12] J. Redmon and A. Farhadi. “YOLO9000: Better, Faster, Stronger”. In: ArXiv e-prints (Dec. 2016). arXiv: 1612.08242 [cs.CV].
[13] Joseph Redmon and Ali Farhadi. “YOLOv3: An Incremental Improvement”. In: arXiv (2018).
[14] Frank Rosenblatt. The Perceptron–a perceiving and recognizing automaton. 85-460-1. Cornell Aeronautical Laboratory, 1957.
[15] Olga Russakovsky, Jia Deng, Hao Su, et al. “ImageNet Large Scale Visual Recognition Challenge”. In: International Journal of Computer Vision 115.3 (2015), pp. 211–252.
[16] David Silver, Aja Huang, Chris J. Maddison, et al. “Mastering the game of Go with deep neural networks and tree search”. In: Nature 529.7587 (Jan. 2016), pp. 484–489.
[17] S. E. Wei, V. Ramakrishna, T. Kanade, et al. “Convolutional Pose Machines”. In: CVPR. 2016, pp. 4724–4732.
[18] Tobias Würfl, Florin C Ghesu, Vincent Christlein, et al. “Deep learning computed tomography”. In: International Conference on Medical Image Computing and Computer-Assisted Springer International Publishing. 2016, pp. 432–440.
 
Further Reading
A gentle Introduction to Deep Learning
Transcript (Summer 2020 !)
Free LME Deep Learning Ressources

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