Learning with Collaborative Neural Network Group by Reflection
This addresses the problem of high training costs and lack of interpretability in neural networks for machine learning practitioners, though it appears incremental as it builds on existing neural network architectures.
The paper tackles the challenge of making large-scale neural network training more efficient and interpretable by introducing the Collaborative Neural Network Group (CNNG), which uses multiple small networks to achieve a 74.5% reduction in error rate and 99.45% accuracy on MNIST in one training epoch.
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.