OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers

TL;DR

OpenMatch introduces an open-set semi-supervised learning method that effectively detects outliers in unlabeled data, improving performance by combining FixMatch with novelty detection and a new regularization loss.

Contribution

The paper proposes a novel open-set SSL approach called OpenMatch, unifying FixMatch with one-vs-all classifiers and a soft-consistency loss for outlier detection.

Findings

Achieves state-of-the-art results on three datasets.

Outperforms fully supervised models in outlier detection on CIFAR10.

Effectively rejects outliers, improving SSL robustness.

Abstract

Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. However, in practice, unlabeled data can contain categories unseen in the labeled set, i.e., outliers, which can significantly harm the performance of SSL algorithms. To address this problem, we propose a novel Open-set Semi-Supervised Learning (OSSL) approach called OpenMatch. Learning representations of inliers while rejecting outliers is essential for the success of OSSL. To this end, OpenMatch unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers. The OVA-classifier outputs the confidence score of a sample being an inlier, providing a threshold to detect outliers. Another key contribution is an open-set soft-consistency regularization loss, which enhances the smoothness of the OVA-classifier with respect to input transformations and greatly improves outlier detection. OpenMatch achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.