Discovering Class-Wise Trends of Max-Pooling in Subspace
Yuchen Zheng, Brian Kenji Iwana, Seiichi Uchida · 2018
The traditional max-pooling operation in Convolutional Neural Networks (CNNs) only obtains the maximal value from a pooling window. However, it discards the information about the precise position of the maximal value. In this paper, we extract the location of the maximal value in a pooling window and transform it into "displacement feature". We analyze and discover the class-wise trend of the displacement features in many ways. The experimental results and discussion demonstrate that the displacement features have beneficial behaviors for solving the problems in max-pooling.