In this tutorial, we will take a closer look at self-supervised contrastive learning. Self-supervised learning, or also sometimes called unsupervised learning, describes the scenario where we have given input data, but no accompanying labels to train in a classical supervised way. However, this data still contains a lot of information from which we can learn: how are the images different from each other? What patterns are descriptive for certain images? Can we cluster the images? To get an insight into these questions, we will implement a popular, simple contrastive learning method, SimCLR, and apply it to the STL10 dataset. This notebook is part of a lecture series on Deep Learning at the University of Amsterdam. The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.
Link to the notebook: https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial17/SimCLR.html
00:00 Introduction
03:55 Setup
04:54 Data Augmentations for Contrastive Learning
09:05 STL10 dataset
12:00 SimCLR implementation
18:55 SimCLR training
