DualMatch: Robust Semi-Supervised Learning with Dual-Level Interaction
Cong Wang Xiaofeng Cao Lanzhe Guo2 Zenglin Shi
Semi-supervised learning provides an expressive framework for exploiting
unlabeled data when labels are insufficient. Previous semi-supervised learning
methods typically match model predictions of different data-augmented views in
a single-level interaction manner, which highly relies on the quality of
pseudo-labels and results in semi-supervised learning not robust. In this
paper, we propose a novel SSL method called DualMatch, in which the class
prediction jointly invokes feature embedding in a dual-level interaction
manner. DualMatch requires consistent regularizations for data augmentation,
specifically, 1) ensuring that different augmented views are regulated with
consistent class predictions, and 2) ensuring that different data of one class
are regulated with similar feature embeddings. Extensive experiments
demonstrate the effectiveness of DualMatch. In the standard SSL setting, the
proposal achieves 9% error reduction compared with SOTA methods, even in a more
challenging class-imbalanced setting, the proposal can still achieve 6% error
reduction. Code is available at https://github.com/CWangAI/DualMatch