The paper introduces a novel method called stochastic pooling to regularize large convolutional neural networks (CNNs). Stochastic pooling replaces the conventional deterministic pooling operations with a stochastic procedure, where the activation within each pooling region is randomly selected according to a multinomial distribution based on the activations in that region. This approach is hyper-parameter free and can be combined with other regularization techniques like dropout and data augmentation. The authors demonstrate that stochastic pooling achieves state-of-the-art performance on four image datasets (MNIST, CIFAR-10, CIFAR-100, and SVHN) compared to methods that do not use data augmentation. The key advantage of stochastic pooling is its ability to prevent overfitting, especially in deep CNNs, by incorporating non-maximal activations and introducing noise during training. The method is also shown to be computationally efficient and easy to integrate into existing CNN architectures.The paper introduces a novel method called stochastic pooling to regularize large convolutional neural networks (CNNs). Stochastic pooling replaces the conventional deterministic pooling operations with a stochastic procedure, where the activation within each pooling region is randomly selected according to a multinomial distribution based on the activations in that region. This approach is hyper-parameter free and can be combined with other regularization techniques like dropout and data augmentation. The authors demonstrate that stochastic pooling achieves state-of-the-art performance on four image datasets (MNIST, CIFAR-10, CIFAR-100, and SVHN) compared to methods that do not use data augmentation. The key advantage of stochastic pooling is its ability to prevent overfitting, especially in deep CNNs, by incorporating non-maximal activations and introducing noise during training. The method is also shown to be computationally efficient and easy to integrate into existing CNN architectures.