Generalized Stochastic Pooling

Pooling layers play a critical role in Convolutional NeuralNetworks by reducing spatial dimensions and enhancing translation invariance.While conventional methods like max pooling and average pooling are effective, they can respectively amplify noise or dilute important features.Stochastic pooling introduces probabilistic sampling to improve generalization but is susceptible to biases from outliers, often mimicking max pooling in such cases.To address these limitations, we propose a generalization of stochastic pooling that introduces a tunable parameter to control the balance between uniform sampling, stochastic pooling, and max pooling.Experiments on multiple datasets demonstrate that uniform sampling outperforms the biased one, achieving a favorable trade-off between regularization and performance.