BYOL tutorial: self-supervised learning on CIFAR images with code in Pytorch
Implement and understand byol, a self-supervised computer vision method without negative samples. Learn how BYOL learns robust representations for image classification.
Self-supervised learning tutorial: Implementing SimCLR with pytorch lightning
Learn how to implement the infamous contrastive self-supervised learning method called SimCLR. Step by step implementation in PyTorch and PyTorch-lightning
Understanding SWAV: self-supervised learning with contrasting cluster assignments
A mathematical explanation of the Swapping Assignments Between Views (SWAV) paper.
Grokking self-supervised (representation) learning: how it works in computer vision and why
A general perspective on understanding self-supervised representation learning methods.
The theory behind Latent Variable Models: formulating a Variational Autoencoder
Explaining the mathematics behind generative learning and latent variable models and how Variational Autoencoders (VAE) were formulated (code included)
Self-supervised representation learning on videos
In this article, we dive into the state-of-the-art methods on self-supervised representation learning in computer vision, by carefully reviewing the fundamentals concepts of self-supervision on learning video representations.
Graph Neural Networks - An overview
How Neural Networks can be used in graph data
How to Generate Images using Autoencoders
Learn what autoencoders are and build one to generate new images
Document clustering
Use unsupervised learning to cluster documents based on their content