A improved pooling method for convolutional neural networks

The pooling layer in convolutional neural networks plays a crucial role in reducing spatial dimensions, and improving computational efficiency. However, standard pooling operations such as max pooling or average pooling are not suitable for all applications and data types. Therefore, developing custom pooling layers that can adaptively learn and extract relevant features from specific datasets is of great significance. In this paper, we propose a novel approach to design and implement customizable pooling layers to enhance feature extraction capabilities in CNNs. The proposed T-Max-Avg pooling layer incorporates a threshold parameter T, which selects the K highest interacting pixels as specified, allowing it…

• NN
  National Natural Science Foundation of China

  Award: 61662043
• SA
  Science and Technology Program of Gansu Province

  Awards: 21JR7RA288, 21JR7RE174