Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning

TL;DR

This paper introduces Dual Student, a semi-supervised learning method that replaces the traditional teacher model with a second student, leading to significant performance improvements across benchmarks.

Contribution

The paper proposes a novel dual student framework that replaces the EMA teacher with another student, addressing performance bottlenecks in consistency-based SSL methods.

Findings

Reduces error rate on CIFAR-10 from 16.84% to 12.39%.

Improves CIFAR-100 error rate from 34.10% to 31.56%.

Enhances domain adaptation performance.

Abstract

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.