Recently, consistency-based methods have achieved state-of-the-art results in
semi-supervised learning (SSL). These methods always involve two roles, an
explicit or implicit teacher model and a student model, and penalize
predictions under different perturbations by a consistency constraint. However,
the weights of these two roles are tightly coupled since the teacher is
essentially an exponential moving average (EMA) of the student. In this work,
we show that the coupled EMA teacher causes a performance bottleneck. To
address this problem, we introduce Dual Student, which replaces the teacher
with another student. We also define a novel concept, stable sample, following
which a stabilization constraint is designed for our structure to be trainable.
Further, we discuss two variants of our method, which produce even higher
performance. Extensive experiments show that our method improves the
classification performance significantly on several main SSL benchmarks.
Specifically, it reduces the error rate of the 13-layer CNN from 16.84% to
12.39% on CIFAR-10 with 1k labels and from 34.10% to 31.56% on CIFAR-100 with
10k labels. In addition, our method also achieves a clear improvement in domain
adaptation.

本文介绍一种基于一致性方法来改善半监督学习（SSL）效果的新模型 —— 双生学生（Dual Student），它将原模型中的教师模型替换为另一个学生模型；还引入了一个新的概念 —— 稳定样本，利用它来设计一个稳定约束以便于模型的训练；本文还讨论了新模型的两种变体，并在 SSL 基准测试中对其进行了广泛的实验，证明了本文提出的新模型可以显著提高分类性能和领域适应性。