The course is devoted to the methods of supervised learning applied in computer vision and in particular to the innovative group of methods, such as Deep Learning. During the course various aspects of Deep learning will be covered. The fundamentals in neural networks such as MLP will be presented. Then, the focus will be on the variety of convolutional neural networks and associated problems, such design of architectures, data preparation, optimization methods, fine tuning/domain adaptation and fusion with Deep architectures. The temporality aspects will also be considered for various computer vision, CBIR and CVIR applications. In practical work students will acquire skills for the use of optimal network configurations accordingly to the available OpenSource frameworks.
V. Vapnik, “The Nature of Statistical Learning Theory”, series Statistics for Engineering and Information Sciences, 2010.
I. Goodfellow, Y. Bengio, “Deep Learning”, series Adaptive Computation and Machine Learning, MIT Press, 2016.
J. Benois-Pineau, P. Le Callet, “Visual Content Indexing and Retrieval With Psycho-visual Models”, Eds, Springer, 2017.
R. Girshick, J. Donahue, T. Darrell, J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation”, arXiv:1311.2524
G. Csurka, Domain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition, Springer 2017,
B. Chu, V. Madhavan, O. Beijbom, J. Hoffman, T. Darrell, “Best Practices for Fine-Tuning Visual Classifiers to New Domains”. ECCV Workshops (3) 2016: 435-442
Additional lecturers, if exist(name, position, degree): assistants (PhD students…)