PristupačnostModels for representing images and video
Data is displayed for the academic year: 2025./2026.
Lecturers
Course Description
Short overview of classic computer vision approaches. Comparing histograms of visual words with deep convolutional models. Image classification. Batchnorm, residual and skip connections, knowledge transfer. Computer vision tasks. Hardware for training deep models and performing inference. Trends. Details of image-classification architectures. Interpretation of deep models. What and how deep models learn. Deep models for dense prediction. Adversarial models for image generation.
Study Programmes
Postgraduate doctoral study programme
Literature
Christopher M. Bishop (2016.), Pattern Recognition and Machine Learning, Springer
Simon J. D. Prince (2012.), Computer Vision, Cambridge University Press
Richard Szeliski (2010.), Computer Vision, Springer Science & Business Media
Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016.), Deep Learning, MIT Press
General
ID 154873
Summer semester
6 ECTS
L1 e-Learning