Convolutional Neural Networks (CNNs) are well established models capable of
achieving state-of-the-art classification accuracy for various computer vision
tasks. However, they are becoming increasingly larger, using millions of
parameters, while they are restricted to handling images of fixed size. In this
paper, a quantization-based approach, inspired from the well-known
Bag-of-Features model, is proposed to overcome these limitations. The proposed
approach, called Convolutional BoF (CBoF), uses RBF neurons to quantize the
information extracted from the convolutional layers and it is able to natively
classify images of various sizes as well as to significantly reduce the number
of parameters in the network. In contrast to other global pooling operators and
CNN compression techniques the proposed method utilizes a trainable pooling
layer that it is end-to-end differentiable, allowing the network to be trained
using regular back-propagation and to achieve greater distribution shift
invariance than competitive methods. The ability of the proposed method to
reduce the parameters of the network and increase the classification accuracy
over other state-of-the-art techniques is demonstrated using three image
datasets.

本文提出了一种基于量化的方法，称为 Convolutional BoF，使用 RBF 神经元对从卷积层中提取的信息进行量化，并且能够原生地分类各种大小的图像，同时显著减少了网络中的参数，通过三个图像数据集的实验证明了该方法降低了网络参数并提高了分类精度。