Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. FixMatch first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 - just 4 labels per class. We carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success.

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. FixMatch first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction.

Four knowledgeable reviewers support acceptance for the contributions. Reviewers find that i) the proposed algorithm is simple; ii) efficient and empirical evaluation is very carefully designed with an extensive ablation study; iii) analysis on augmentation strategy and sharpening also provide good insights. Therefore, I also recommend acceptance. However, please consider revising your paper to address all the concerns and comments from the reviewers.

I cite from ReMixMatch figure caption: "Augmentation anchoring. We use the prediction for a weakly augmented image (green, middle) as the target for predictions on strong augmentations of the same image". This sounds to me as a summary of the presented work, and as such I consider it a special case of the ReMixMatch. Authors have discussed the differences between their work and ReMixMatch, mentioning that (1) "ReMixMatch don t use pseudo labeling", and (2) ReMixMatch uses sharpening of pseudolabels and weight annealing of the unlabeled data loss. However, in section 3.2.1 of ReMixMatch, it is stated that the guessed labels are used as targets (for strongly augmented images) using cross-entropy loss.

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. FixMatch first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction.