  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.
