MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks
This is an incremental improvement for researchers and practitioners in machine learning, addressing regularization in CNNs.
The paper tackles overfitting in deep learning by introducing MaxDropoutV2, an improved regularization method for convolutional neural networks, which shows faster training and higher accuracy on two public datasets compared to its predecessor.
In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process since training complex models denotes an expensive task and results are prone to overfit the training data. A supervised regularization technique called MaxDropout was recently proposed to tackle the latter, providing several improvements concerning traditional regularization approaches. In this paper, we present its improved version called MaxDropoutV2. Results considering two public datasets show that the model performs faster than the standard version and, in most cases, provides more accurate results.