Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning
This work addresses computational resource challenges for researchers and practitioners using distributed CNN training, but it is incremental as it builds on existing knowledge without introducing new methods.
The study tackled the problem of understanding how CNN architectures and data augmentation affect model accuracy and computational efficiency in distributed training, finding insights for optimizing deployment in resource-intensive scenarios.
Convolutional Neural Networks (CNNs) have proven to be highly effective in solving a broad spectrum of computer vision tasks, such as classification, identification, and segmentation. These methods can be deployed in both centralized and distributed environments, depending on the computational demands of the task. While much of the literature has focused on the explainability of CNNs, which is essential for building trust and confidence in their predictions, there remains a gap in understanding their impact on computational resources, particularly in distributed training contexts. In this study, we analyze how CNN architectures primarily influence model accuracy and investigate additional factors that affect computational efficiency in distributed systems. Our findings contribute valuable insights for optimizing the deployment of CNNs in resource-intensive scenarios, paving the way for further exploration of variables critical to distributed learning.