A Novel on Conditional Min Pooling and Restructured Convolutional Neural Network

Abstract

There is no doubt that CNN has made remarkable technological developments as the core technology of computer vision, but the pooling technique used for CNN has its own issues. This study set out to solve the issues of the pooling technique by proposing conditional min pooling and a restructured convolutional neural network that improved the pooling structure to ensure efficient use of the conditional min pooling. Some Caltech 101 and crawling data were used to test the performance of the conditional min pooling and restructured convolutional neural network. The pooling performance test based on Caltech 101 increased in accuracy by 0.16~0.52% and decreased in loss by 19.98~28.71% compared with the old pooling technique. The restructured convolutional neural network did not have a big improvement in performance compared to the old algorithm, but it provided significant outcomes with similar performance results to the algorithm. This paper presents the results that the loss rate was reduced rather than the accuracy rate, and this result was achieved without the improvement of convolution.