MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs
This work addresses a fundamental bottleneck in CNN design for pixel-level prediction tasks, offering a more efficient and effective alternative to standard pooling methods.
The paper tackled the problem of limited pooling and unpooling operations in CNNs by introducing MorphPool, a method based on mathematical morphology that generalizes max pooling and improves unpooling. The result showed improved predictive performance with significantly reduced parameters, as demonstrated on two tasks and three large-scale datasets.
Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. It is common to combine unpooling with convolution or deconvolution for up-sampling. However, using its morphological properties, unpooling can be generalised and improved. Extensive experimentation on two tasks and three large-scale datasets shows that morphological pooling and unpooling lead to improved predictive performance at much reduced parameter counts.