# Stochastic Pooling for Regularization of Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1301.3557 · HTML: https://mlsanity.com/paper/1301.3557/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2013.

## Built on by (most cited first, 30 of 48)

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- [Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics](https://mlsanity.com/paper/2307.12660/) (2023)
- [Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability](https://mlsanity.com/paper/2107.08461/) (2021)
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- [Block shuffling learning for Deepfake Detection](https://mlsanity.com/paper/2202.02819/) (2022)
- [Learnable Discrete Wavelet Pooling (LDW-Pooling) For Convolutional Networks](https://mlsanity.com/paper/2109.06638/) (2021)
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- [KeepOriginalAugment: Single Image-based Better Information-Preserving Data Augmentation Approach](https://mlsanity.com/paper/2405.06354/) (2024)
- [Fuzzy Pooling](https://mlsanity.com/paper/2202.08372/) (2022)
- [Data Dimension Reduction makes ML Algorithms efficient](https://mlsanity.com/paper/2211.09392/) (2022)
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- [Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition](https://mlsanity.com/paper/2409.09216/) (2024)
- [Maximal function pooling with applications](https://mlsanity.com/paper/2103.01292/) (2021)
- [Neuron-Specific Dropout: A Deterministic Regularization Technique to Prevent Neural Networks from Overfitting & Reduce Dependence on Large Training Samples](https://mlsanity.com/paper/2201.06938/) (2022)
- [Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge](https://mlsanity.com/paper/2202.09248/) (2022)
- [An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images](https://mlsanity.com/paper/2204.01341/) (2022)
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- [AMD-HookNet++: Evolution of AMD-HookNet with Hybrid CNN-Transformer Feature Enhancement for Glacier Calving Front Segmentation](https://mlsanity.com/paper/2512.14639/) (2025)

## Abstract (arXiv)

We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.