# MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2203.02740 · HTML: https://mlsanity.com/paper/2203.02740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 5 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Variational Dropout Sparsifies Deep Neural Networks](https://mlsanity.com/paper/1701.05369/) (2017)
- [Concrete Dropout](https://mlsanity.com/paper/1705.07832/) (2017)
- [ImageNet pre-trained models with batch normalization](https://mlsanity.com/paper/1612.01452/) (2016)
- [LocalDrop: A Hybrid Regularization for Deep Neural Networks](https://mlsanity.com/paper/2103.00719/) (2021)
- [MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values](https://mlsanity.com/paper/2007.13723/) (2020)
- [Intestinal Parasites Classification Using Deep Belief Networks](https://mlsanity.com/paper/2101.06747/) (2021)
- [Energy-based Dropout in Restricted Boltzmann Machines: Why not go random](https://mlsanity.com/paper/2101.06741/) (2021)

## Abstract (arXiv)

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.