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Paper Link: https://arxiv.org/pdf/2001.07685 What Problem Does FixMatch Address? FixMatch is a semi-supervised learning (SSL) algorithm designed to solve two long-standing technical challenges using a unified and simple framework. In many real-world machine learning applications, labeled data is expensive and time-consuming to obtain, while unlabeled data is abundant. FixMatch addresses this imbalance by combining two powerful ideas in SSL: Consistency Regularization: The assumption that a model should produce consistent predictions when the input undergoes small augmentations or perturbations. Pseudo-Labeling: Treating high-confidence predictions on unlabeled data as if they were ground truth labels for training purposes. While previous SSL methods often combined these ideas through complex architectures or training pipelines, FixMatch simplifies the process using a confidence threshold and a two-stage data augmentation strategy to achieve state-of-the-art performance ...