Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks

TL;DR

This survey reviews recent regularization methods for CNNs, categorizing them into data augmentation, internal modifications, and label transformations, emphasizing reproducibility and recent developments to prevent overfitting.

Contribution

It provides a recent, reproducible overview of CNN regularization techniques from the last five years, classified into three main categories.

Findings

Significant improvements in CNN performance due to regularization methods

Reproducibility is emphasized with publicly available code

Recent techniques focus on data augmentation, internal changes, and label transformations

Abstract

Several image processing tasks, such as image classification and object detection, have been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and EfficientNet, many architectures have achieved outstanding results in at least one dataset by the time of their creation. A critical factor in training concerns the network's regularization, which prevents the structure from overfitting. This work analyzes several regularization methods developed in the last few years, showing significant improvements for different CNN models. The works are classified into three main areas: the first one is called "data augmentation", where all the techniques focus on performing changes in the input data. The second, named "internal changes", which aims to describe procedures to modify the feature maps generated by the neural network or the kernels. The last one, called "label", concerns transforming the labels of a given input. This work presents two main differences comparing to other available surveys about regularization: (i) the first concerns the papers gathered in the manuscript, which are not older than five years, and (ii) the second distinction is about reproducibility, i.e., all works refered here have their code available in public repositories or they have been directly implemented in some framework, such as TensorFlow or Torch.