The proposed stochastic average pooling (SAP) module can be extended to various neural network architectures by integrating it into different layers where average pooling is traditionally used. For instance, in recurrent neural networks (RNNs) or long short-term memory (LSTM) networks, SAP can be applied to the output of the hidden states to introduce stochasticity and regularization effects similar to those observed in convolutional networks. Additionally, in transformer architectures, where attention mechanisms are prevalent, SAP can be utilized in the pooling layers that aggregate information across different attention heads or in the final output layer to enhance generalization.
Moreover, SAP can be adapted for use in generative models, such as Generative Adversarial Networks (GANs), where it can be employed in the discriminator to improve robustness against overfitting. By incorporating stochastic average pooling in the generator, it may also help in producing more diverse outputs. Furthermore, SAP can be integrated into unsupervised learning frameworks, such as autoencoders, to enhance feature extraction by introducing variability in the pooling process, thereby improving the model's ability to generalize from the training data.