# FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning Source: [FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:16:12.526Z Authors: Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Zsolt Kira Published: 2020-07-16T17:55:31.000Z ## Abstract Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components. Such methods, however, are limited to simple transformations such as traditional data augmentation or convex combinations of two images. In this paper, we propose a novel learned feature-based refinement and augmentation method that produces a varied set of complex transformations. Importantly, these transformations also use information from both within-class and across-class prototypical representations that we extract through clustering. We use features already computed across iterations by storing them in a memory bank, obviating the need for significant extra computation. These transformations, combined with traditional image-based augmentation, are then used as part of the consistency-based regularization loss. We demonstrate that our method is comparable to current state of art for smaller datasets (CIFAR-10 and SVHN) while being able to scale up to larger datasets such as CIFAR-100 and mini-Imagenet where we achieve significant gains over the state of art (\\textit{e.g.,} absolute 17.44\\% gain on mini-ImageNet). We further test our method on DomainNet, demonstrating better robustness to out-of-domain unlabeled data, and perform rigorous ablations and analysis to validate the method. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on SVHN (test)](): Image Classification; SVHN (test); Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()