Title: A SINGLE SHOT PCA-DRIVEN ANALYSIS OF NETWORK STRUCTURE TO REMOVE REDUNDANCY. Abstract: Deep learning models have outperformed traditional methods in many fields such
as natural language processing and computer vision. However, despite their
tremendous success, the methods of designing optimal Convolutional Neural Networks
(CNNs) are still based on heuristics or grid search. The resulting networks
obtained using these techniques are often overparametrized with huge computational
and memory requirements. This paper focuses on a structured, explainable
approach towards optimal model design that maximizes accuracy while keeping
computational costs tractable. We propose a single-shot analysis of a trained CNN
that uses Principal Component Analysis (PCA) to determine the number of filters
that are doing significant transformations per layer, without the need for retraining.
It can be interpreted as identifying the dimensionality of the hypothesis space
under consideration. The proposed technique also helps estimate an optimal number
of layers by looking at the expansion of dimensions as the model gets deeper.
This analysis can be used to design an optimal structure of a given network on
a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate
these techniques by optimizing VGG and AlexNet networks on CIFAR-10,
CIFAR-100 and ImageNet datasets.