LEARNABLE DISCRETE WAVELET POOLING (LDW-POOLING) FOR CONVOLUTIONAL NETWORKS

Pooling is a simple but widely used layer in modern deep CNN architectures for feature aggregation and extraction. Typical CNN design focuses on the conv layers and activation functions, while leaving the pooling layers with fewer options. We introduce the Learning Discrete Wavelet Pooling (LDW-Pooling) that can be applied universally to replace standard pooling operations to better extract features with improved accuracy and efficiency.