Advancements in Deep Learning Architectures for Image Recognition

Abstract

This paper comprehensively reviews recent advancements in deep learning architectures for image recognition tasks. Key innovations in convolutional neural networks (CNNs), including novel architectures such as ResNet, DenseNet, and EfficientNet, have significantly improved the performance of image recognition systems. Additionally, techniques such as attention mechanisms, capsule networks, and graph neural networks enhance the ability of models to capture complex spatial and semantic relationships within images. Furthermore, the role of transfer learning and domain adaptation in leveraging pre-trained models to address data scarcity and domain shift issues is investigated. Finally, challenges and future directions in the field are discussed, including interpretability, robustness, and scalability. By synthesizing recent research findings, this paper aims to provide insights into the state-of-the-art in deep learning architectures for image recognition and inspire future research directions in this rapidly evolving field.