FixMatch - Simplifying Semi-Supervised Learning with Consistency and Confidence

Explore a groundbreaking approach to semi-supervised learning in this 20-minute video lecture. Dive into FixMatch, a simple yet highly effective method that combines consistency regularization and pseudo-labeling to achieve state-of-the-art performance in scenarios with limited labeled examples. Understand the algorithm's process of generating pseudo-labels from weakly-augmented unlabeled images and training the model on strongly-augmented versions. Examine the impressive results, including 94.93% accuracy on CIFAR-10 with just 250 labels and 88.61% accuracy with only 40 labels. Gain insights into the key factors contributing to FixMatch's success through an extensive ablation study. Learn about semisupervised learning, supervised loss, weak and strong augmentation techniques, and the underlying hypotheses behind this innovative approach.

Introduction
What is Semisupervised Learning
Supervised Loss
Weekly Augmented
Strong Augmentation
Pseudolabel
What does it learn
Hypothesis
Results