Regularisation Effects Differ Across Datasets and Model Types, Study Finds

Global: Regularisation Effects Differ Across Datasets and Model Types, Study Finds

Researchers analyzing neural network regularisation reported that the presumed universal performance boost from regularisation techniques does not hold uniformly. In a systematic review and empirical evaluation covering ten numerical and image datasets, the team compared the impact of various regularisation strategies on multi-layer perceptron (MLP) and convolutional neural network (CNN) architectures. The findings indicate that the efficacy of regularisation is highly dependent on the data domain and model configuration.

Taxonomy of Regularisation Methods

The authors organized regularisation approaches into four broad categories: data‑based strategies, architecture strategies, training strategies, and loss‑function strategies. This framework highlights both overlapping mechanisms and distinct objectives among techniques such as data augmentation, batch normalisation, dropout, and specialised loss terms.

Experimental Setup

To assess the practical impact of each category, the study applied the methods to ten publicly available datasets—five numeric and five image‑based—using standard MLP and CNN pipelines. Performance was measured primarily through classification accuracy, with additional attention to training stability and convergence speed.

Results on Numerical Datasets

On numeric datasets, the inclusion of explicit regularisation terms in the loss function yielded measurable improvements in accuracy, while architectural adjustments like batch normalisation showed limited effect. The authors note that these gains were consistent across both MLP and CNN models for the numeric tasks.

Results on Image Datasets

Conversely, for image datasets, batch normalisation emerged as the most beneficial technique, enhancing performance across both model families. Other regularisation methods, including certain loss‑function penalties, did not produce consistent improvements and in some cases degraded accuracy.

Interpretation of Findings

The divergent outcomes suggest that regularisation interacts with data characteristics and model structures in nuanced ways. The authors point out contradictions—such as techniques that aid one data type but hinder another—and correspondences, where multiple methods converge on similar performance gains under specific conditions.

Practical Implications

Practitioners are advised to evaluate regularisation choices in the context of their specific datasets rather than applying them indiscriminately. Tailoring regularisation strategies to the data domain may lead to more reliable generalisation and avoid unnecessary computational overhead.

Future Directions

The study recommends further investigation into hybrid regularisation schemes and the development of diagnostic tools that predict technique suitability based on dataset properties. Expanding the analysis to additional model architectures and larger-scale tasks could refine the taxonomy and guide best‑practice guidelines.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.