Robust Semi-supervised Learning by Wisely Leveraging Open-set Data

TL;DR

This paper introduces WiseOpen, a novel framework for open-set semi-supervised learning that selectively leverages open-set data using a gradient-variance-based mechanism to improve in-distribution classification performance.

Contribution

The paper proposes WiseOpen, a theoretically motivated, selective open-set data utilization strategy for semi-supervised learning that outperforms existing methods.

Findings

WiseOpen improves ID classification accuracy.

Selective data use enhances robustness against OOD data.

Practical variants reduce computational costs.

Abstract

Open-set Semi-supervised Learning (OSSL) holds a realistic setting that unlabeled data may come from classes unseen in the labeled set, i.e., out-of-distribution (OOD) data, which could cause performance degradation in conventional SSL models. To handle this issue, except for the traditional in-distribution (ID) classifier, some existing OSSL approaches employ an extra OOD detection module to avoid the potential negative impact of the OOD data. Nevertheless, these approaches typically employ the entire set of open-set data during their training process, which may contain data unfriendly to the OSSL task that can negatively influence the model performance. This inspires us to develop a robust open-set data selection strategy for OSSL. Through a theoretical understanding from the perspective of learning theory, we propose Wise Open-set Semi-supervised Learning (WiseOpen), a generic OSSL framework that selectively leverages the open-set data for training the model. By applying a gradient-variance-based selection mechanism, WiseOpen exploits a friendly subset instead of the whole open-set dataset to enhance the model's capability of ID classification. Moreover, to reduce the computational expense, we also propose two practical variants of WiseOpen by adopting low-frequency update and loss-based selection respectively. Extensive experiments demonstrate the effectiveness of WiseOpen in comparison with the state-of-the-art.