# Reducing Model Complexity in Neural Networks by Using Pyramid Training Approaches
## 6 Conclusions  
Our exploration of the pyramid training schema, along with the strategic computation of feature size reduction using similarity comparison, has yielded valuable insights into enhancing neural network architectures for classification tasks. The iterative pyramid training process, combined with feature size reduction, has proven to be an effective strategy in refining the model's adaptability and computational efficiency. ReducedNetwork, which integrates these methodologies, demonstrated a commendable reduction in model size while maintaining test set accuracy comparable to BaseNetwork. The application of quantization, a model compression technique, to both BaseNetwork and ReducedNetwork, further underscored the versatility of our approach. The combination with quantization in the Reduced+Quantization configuration achieved the smallest model size, with a sixfold reduction rate. Although this considerable reduction in size introduced a marginal decrease in test set accuracy, it illustrates the potential for substantial model optimization. The incorporation of similarity comparison in feature size reduction provides several advantages:  
* Efficiency: it significantly reduces redundancy and enhances computational efficiency, crucial for environments with limited computational resources. * Information preservation: this method ensures that essential features are retained, which is vital for maintaining the effectiveness of the model. * Scalability: similarity comparison is flexible and can be applied across different network layers and architectures, making it widely applicable. * Energy conservation: particularly beneficial for mobile and embedded systems, reducing computational demands helps in conserving energy. For segmentation tasks, U-Net and its reduced variant, U-Net-Reduced, were evaluated. U-Net-Reduced demonstrated improved efficiency with a significantly lower model size of 7 MB compared with original U-Net's 31 MB, showcasing the effectiveness of feature dimensionality reduction. However, this reduction in size was accompanied by a slight increase in validation loss, highlighting the typical trade-offs between model complexity and performance. In object detection tasks, CustomBackbone and VGG16, along with their reduced versions, exhibited notable reductions in model size and memory allocation, demonstrating their potential for resource-efficient deployment. The reduced variants showed increased losses, which underscores the delicate balance required when scaling down model complexity to fit resource constraints. Ultimately, the trade-offs between model size reduction and potential increases in loss scores are critical considerations for deploying complex networks in resource-constrained environments. The experimental results, especially with the reduced variants of U-Net and CustomBackbone, demonstrate that feature dimensionality reduction techniques can significantly decrease model size, enabling the operation of sophisticated networks in settings with limited memory and computational resources. This trade-off suggests that sacrificing marginal accuracy for resource efficiency could be a viable strategy, particularly when the computational demands of larger models are prohibitive in real-world applications. Through this study, the effectiveness of similarity comparison in feature size reduction has been highlighted as not only enhancing efficiency but also ensuring the practical deployment of advanced neural architectures in various computational environments. Looking ahead, refining feature size reduction algorithms to enhance efficiency without sacrificing accuracy could yield more robust models. Additionally, future efforts could also focus on optimizing the training process to reduce the considerable increase in training times associated with our method. Conceptualization, methodology, writing paper, S.G.K. ; formal analysis, supervision, B.S. ; providing research ideas, supervision, F.N. ; review, supervision, A.O.O.