Prè-requis
- At least one basic course in Statistics, Probability, Differential Calculus, Optimization and Linear Algebra
- Good knowledge about Statistical Learning
- At least one introductory course about Deep Learning
- At least one basic course in Programming (Python and Pytorch)
- Basic concepts of image processing and computer vision
- Knowledge about Medical Imaging is not necessary
Objectif du cours
Good and expressive data representations can improve the accuracy of machine learning problems and ease interpretability and transfer. For computer vision and medical imaging tasks, handcrafting good data representations, a.k.a. feature engineering, was traditionally hard. Deep Learning has changed this paradigm by allowing the automatic discovery of discriminative, relevant and well-organized representations (i.e., mappings) from data. This is known as representation learning. The objective of this course is to provide an introduction to representation learning in computer vision and medical imaging applications.
Organisation des séances
8 lectures divided into 1,5h of theory and 1,5h of practical session + 1 session of exam
Mode de validation
Grading will be based on the practical session reports (50%) and written or oral exam (depending on the number of students) (50%).
Thèmes abordés
We will cover the following topics:
- Representation Learning
- Transfer Learning
- Domain Adaptation
- Multi-task Learning
- Knowledge Distillation
- Self-Supervised Learning and Foundation models
- Attention and Transformers
- Disentangled Representations using Generative Models
- Uncertainty, Interpretability and Explainability in Neural Networks
Pietro GORI
(Télécom Paris, LTCI)
Loïc LE FOLGOC
(Télécom Paris, LTCI)