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The current architecture is built on the network-in-network approach proposed by Lin et al.[1] for the purpose of increase the representation power of the neural networks. They added additional 1 X 1 convolutional layers, serving as dimension reduction modules to significantly reduce the number of parameters of the model. The paper also took inspiration from the Regions with Convolutional Neural Networks (R-CNN) proposed by Girshick et al. [2]. The overall detection problem is divided into two subproblems:

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The performance of deep neural networks can be improved by increasing the depth and the width of the networks. However, this suffers two major bottlenecks. One disadvantage is that the enlarged network tends to overfit the train data, especially if there is only limited labeled examples. The other drawback is the dramatic increase in computational resources when learning large number of parameters.

The fundamental way of handling both problems would be to use sparsely connected instead of fully connected networks and, at the same time, make numerical calculation on non-uniform sparse data structures efficient. Therefore, the inception architecture was motivated by Arora et al. [3] and Catalyurek et al. [4] and overcome these difficulties by clustering sparse matrices into relatively dense submatrices. It takes advantage of both extra sparsity and existing computational hardware.

By using nearly 5 million parameters, GoogLeNet, compared to previous architectures like VGGNet and AlexNet, reduced the number of parameters in the network by almost 92%. This enabled Inception to be used for many big data applications where a huge amount of data was needed to be processed at a reasonable cost while the computational capacity was limited. However, the Inception network is still complex and susceptible to scaling. If the network is scaled up, large parts of the computational gains can be lost immediately. Also, there was no clear description about the various factors that lead to the design decision of this inception architecture, making it harder to adapt to other applications while maintaining the same computational efficiency. 041b061a72