Enhancing Convolutional Neural Networks via separately trained kernels for digit recognition

Abstract

This paper introduces a novel methodology for pre-training neural networks. Instead of the traditional approach of running a single classifier for all objects, the method used in the research constructs individual classifiers for each object to extract unique features. These classifiers, in essence, act as a series of binary classifiers, each pre-training a distinct set of convolutional kernels. These individually trained kernels are then combined and processed by a comprehensive classifier. The methodology leverages the power of individual feature extraction and collaborative processing to enhance the overall performance of the classifier. The results demonstrate that this innovative pre-training approach leads to a significant improvement in classification performance, yielding higher accuracy compared to conventional methods. It underscores the benefits of incorporating individual object feature extraction and combined processing in the pre-training phase of neural network-based classification tasks. The study opens new paths for improving pre-training methods in neural networks, with potential applications in various fields that require high-accuracy object recognition. Future work will delve deeper into the potential challenges associated with model complexity and overfitting, and will also include a more comprehensive evaluation using extensive training, cross-validation, and independent test sets. This will further validate the effectiveness of our proposed pre-training methodology.