Deep Learning for Vision (DLV)

Description

This course studies the typical architectures and applications of deep learning models to computer vision problems such as image classification, object detection, semantic segmentation and visual content generation. After recalling the basis of learning with neural networks and the main tools to understand modern neural architectures, we will dive into the typical methods developed to tackle computer vision problems.

Keywords

Visual representations, convolutional neural networks, classification, regression, object detection.

Prerequisites

Basic knowledge of Linear Algebra, Calculus, Probabilities, Machine Learning, Python, C++

Contents

Part 1 - Bases in deep learning (9h, Elisa Fromont)
  • Intro ML and main computer vision (learning) problems (1h30)
  • NN learning bases (3h30)
  • Perceptron, MLP, Backprop
  • Deep learning (4h)
  • Convolutional Neural Networks
  • Recurrent Neural Networks (LSTM, GRU)
  • Attention Mechanisms (Transformers,...)
Part 2 - Deep learning for vision (12h, Denis Coquenet)
  • Vision architectures for feature extraction (VGG, Resnet, Vision Transformer) : 3h00
  • Object Detection dedicated architectures (YOLO, RCNN) : 3h
  • Semantic segmentation architectures (FCN, U-Net, ...) : 1h30
  • Generative models for vision : 3h
  • GAN
  • Diffusion Models
  • Applications (Handwriting recognition) : 1h30

Teachers

Elisa Fromont (responsible), Denis Coquenet