  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.
