Yes, alternatives to traditional pooling layers include:
Strided Convolutions: Use convolutional layers with a stride greater than 1 to down-sample the feature maps while learning features.
Adaptive Pooling: Allows pooling sizes to be adjusted dynamically based on the input dimensions or requirements.
Global Average Pooling: Reduces the entire feature map to a single value per channel, which can be useful for classification tasks.