Descriptif
Good and expressive data representations can improve the accuracy of machine learning problems and ease interpretability and transfer. For vision tasks, handcrafting good data representations, a.k.a. feature engineering, was traditionally hard. Deep Learning has changed this paradigm by allowing to automatically discover good representations 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.
We will cover the following subjects:
- Introduction to Representation Learning for Vision
- Transfer Learning and Domain Adaptation
- Self-supervised and Contrastive Learning
- Knowledge Distillation
- Disentangled Representations
- Conditional Generative models
- Attention and Transformers
- Visualisation and interpretability in Neural Networks
- Multimodal representation learning and Foundation models
Diplôme(s) concerné(s)
- M2 DS - Data Science
- Master M2 - Mathématiques, Vision, Apprentissage
- Master M2 - Data & Artificial Intelligence
- Master M1 - Interaction, Graphic & Design
- Master M1 - Data and Artificial Intelligence
Parcours de rattachement
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Master M2 - Data & Artificial Intelligence
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 3 ECTS
Pour les étudiants du diplôme M2 DS - Data Science
Pour les étudiants du diplôme Master M2 - Mathématiques, Vision, Apprentissage
Pour les étudiants du diplôme Master M1 - Data and Artificial Intelligence
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 3 ECTS