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Exploring the Stochastic Regularisation in Normalisation Layers for Semi-Supervised Learning.

Changrui Chen, Jungong Han, Kurt Debattista

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 16, 2026

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Semi-supervised learning benefits from moderate stochastic regularization in normalization layers. Novel Shuffle Layer Normalisation (SLN) and Shuffle Group Normalisation (SGN) enhance model robustness and performance.

Area of Science:

Deep Learning
Machine Learning
Computer Vision
Natural Language Processing
Audio Processing

Background:

Semi-supervised learning (SSL) reduces reliance on labeled data but often overlooks architectural suitability.
Existing normalization layers like Batch Normalization (BN), Group Normalization (GN), and Layer Normalization (LN) have limitations in SSL.
Unrestricted stochastic regularization from BN can degrade performance with mismatched label distributions; deterministic methods like GN/LN can be suboptimal.

Purpose of the Study:

To investigate the impact of normalization layer stochastic regularization on semi-supervised learning.
To propose novel normalization techniques that introduce controllable randomness for improved SSL performance.
To address confirmation bias and enhance the robustness and effectiveness of SSL models.

Related Experiment Videos

Main Methods:

Analysis of stochastic regularization's impact on optimization gradient stability in SSL.
Proposal of Shuffle Layer Normalisation (SLN) and Shuffle Group Normalisation (SGN) introducing controllable randomness.
Experimental validation across diverse datasets (image, text, audio) using state-of-the-art SSL algorithms.

Main Results:

The degree of stochastic regularization critically affects optimization gradient stability and SSL performance.
SLN/SGN introduce controllable randomness into deterministic normalization layers without increasing parameters.
Experiments show significant performance enhancement of SSL algorithms using SLN/SGN across modalities.

Conclusions:

A moderate level of stochastic regularization is crucial for robust convergence and generalization in SSL.
SLN/SGN offer a novel approach to enhance SSL by providing tunable regularization.
The proposed methods are compatible with pre-trained parameters and various backbone architectures (CNNs, Transformers).