Learning with Collaborative Neural Network Group by Reflection

TL;DR

This paper introduces the Collaborative Neural Network Group (CNNG), a cooperative system of neural networks designed to improve learning efficiency and accuracy, demonstrated by significant error reduction and high performance on MNIST.

Contribution

The paper presents CNNG, a novel collaborative neural network framework that enhances learning efficiency and accuracy through reflection and cooperation among multiple small networks.

Findings

Reduced error rate by 74.5% on MNIST

Achieved 99.45% accuracy with three small networks

Lower training cost compared to traditional methods

Abstract

For the present engineering of neural systems, the preparing of extensive scale learning undertakings generally not just requires a huge neural system with a mind boggling preparing process yet additionally troublesome discover a clarification for genuine applications. In this paper, we might want to present the Collaborative Neural Network Group (CNNG). CNNG is a progression of neural systems that work cooperatively to deal with various errands independently in a similar learning framework. It is advanced from a solitary neural system by reflection. Along these lines, in light of various circumstances removed by the calculation, the CNNG can perform diverse techniques when handling the information. The examples of chose methodology can be seen by human to make profound adapting more reasonable. In our execution, the CNNG is joined by a few moderately little neural systems. We give a progression of examinations to assess the execution of CNNG contrasted with other learning strategies. The CNNG is able to get a higher accuracy with a much lower training cost. We can reduce the error rate by 74.5% and reached the accuracy of 99.45% in MNIST with three feedforward networks (4 layers) in one training epoch.