Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition

Abstract

Convolutional neural networks (CNNs) are becoming more and more popular today. CNNs now have become a popular feature extractor applying to image processing, big data processing, fog computing, etc. CNNs usually consist of several basic units like convolutional unit, pooling unit, activation unit, and so on. In CNNs, conventional pooling methods refer to 2×2 max-pooling and average-pooling, which are applied after the convolutional or ReLU layers. In this paper, we propose a Multiactivation Pooling (MAP) Method to make the CNNs more accurate on classification tasks without increasing depth and trainable parameters. We add more convolutional layers before one pooling layer and expand the pooling region to 4×4, 8×8, 16×16, and even larger. When doing large-scale subsampling, we pick top-k activation, sum up them, and constrain them by a hyperparameter σ. We pick VGG, ALL-CNN, and DenseNets as our baseline models and evaluate our proposed MAP method on benchmark datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. The classification results are competitive.

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“…2) Average Pooling: It has a function u(x,y) (i.e., window function) to the input data, and selects the average value for each input data on the preceding layer feature map [135], [136]…”

“…And the large pooling has M value as 4, 8, 16, which always have a dependency on input image size. So, [136], in their paper, proposed a Multi-activation pooling method in order to satisfy the need of a large pooling region. This method allows top-p activations to pass through the pooling rate.…”

“…Here p indicates the total number of picked activations. If p = M × M , then it means that each and every activation through the computation contributes to the final output of neuron [136]. For the random pooling region X i , we denote the nth-picked activation as act n…”

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“…In addition, the picture could be huge if it is calculated in the number of pixels, for which the program should contain pooling system to combine multiple numbers together. [11][12][13][14][15][16][17][18] The "pooling" function is:…”

“…Besides, for obtaining more possible characteristics, the program could rotate the picture to different angle to mimic any possible visual angle in the reality. First of all, the original two-dimensional array (or matrix) is [[0, 1, 2, 3], [4,5,6,7], [8,9,10,11], [12,13,14,15]]. Then, after reversing the picture, it becomes [[0, 4, 8, 12], [1,5,9,13], [2,6,10,14], [3,7,11,15]], which enables the convolution to cover more characteristics from different angle.…”