Does Removing Pooling Layers from Convolutional Neural Networks Improve Results?

• Published in: SN computer science 2020-09, Vol.1 (5), p.275, Article 275
• Main Authors: Santos, Claudio Filipi Goncalves dos, Moreira, Thierry Pinheiro, Colombo, Danilo, Papa, João Paulo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Computer vision; Data Structures and Information Theory; Datasets; Deep learning; Hypotheses; Identification; Information Systems and Communication Service; Neural networks; Original Research; Pattern Recognition and Graphics; Software Engineering/Programming and Operating Systems; Vision
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-020-00295-9

Due to their number of parameters, convolutional neural networks are known to take long training periods and extended inference time. Learning may take so much computational power that it requires a costly machine and, sometimes, weeks for training. In this context, there is a trend already in motion to replace convolutional pooling layers for a stride operation in the previous layer to save time. In this work, we evaluate the speedup of such an approach and how it trades off with accuracy loss in multiple computer vision domains, deep neural architectures, and datasets. The results showed significant acceleration with an almost negligible loss in accuracy, when any, which is a further indication that convolutional pooling on deep learning performs redundant calculations.