Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components

This paper presents an exploration of unsupervised methods for initializing and training filters in convolutional layers, aiming to reduce the dependency on labeled data and computational resources. We propose two unsupervised methods based on the distribution of input data and evaluate their performance against traditional Glorot Uniform initialization. By initializing solely the initial layer of a basic CNN network with one of our proposed methods, we attained a 0.78\% enhancement in final accuracy compared to traditional Glorot Uniform initialization. Our findings suggest that these unsupervised methods could serve as effective alternatives for filter initialization, potentially leading to more efficient training processes and a better understanding of CNNs.