Application of Interpolation Pooling in Convolutional Neural Networks
Gaihua Wang · Helix · 2018
In the existing convolutional neural networks, the majority of the used pooling operations are max pooling or mean pooling, but it would lose some important feature information when processing the feature maps.Here we report interpolation pooling to overcome the problem for retaining more effective information of feature maps.The interpolation pooling takes the known pixel points of 4x4 with the nearest to the interpolation point into account.Due to the distance from the pixels to be inserted, the weight of the pixels near the distance in the calculation is larger.We apply it to different convolutional neural networks, such as lenet-5 and pyramid convolutional neural networks.We found that the method has the advantages of faster convergence and higher accuracy than the traditional method of pooling.