Expanding Vision, Enhancing Detail: Evaluating the Impact of Dilation Rates on CNN Performance in Image Classification

Abstract

Context: In the evolving landscape of deep learning, the architecture of convolutional neural networks (CNNs) plays a crucial role in determining their efficiency and effectiveness across various applications.

Problem: A significant challenge in deploying CNNs lies in optimizing their receptive fields to balance detail-oriented and context-aware processing without incurring high computational…