Cutting-Edge Neural Network Architectures for Image Analysis: Harnessing Convolutional, Recurrent, and Generative Adversarial Networks in Deep Learning

EasyChair Preprint 12809

Abstract

In recent years, deep learning has revolutionized image analysis by introducing highly effective neural network architectures. This paper explores cutting-edge approaches in the field, focusing on the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). CNNs have emerged as the cornerstone for image analysis tasks due to their ability to effectively capture spatial hierarchies in visual data. However, the sequential nature of many image-related tasks demands models that can understand temporal dependencies, leading to the incorporation of RNNs. Furthermore, GANs offer a unique framework for generating synthetic data that closely resembles real images, facilitating data augmentation and domain adaptation tasks. This paper provides a comprehensive overview of these neural network architectures, highlighting their individual strengths and synergies when applied to image analysis tasks. Through case studies and experimental results, we demonstrate the efficacy of combining CNNs, RNNs, and GANs for various applications, including image classification, object detection, semantic segmentation, and image generation. We also discuss challenges and future directions in leveraging these advanced architectures to further advance the field of image analysis.

Keyphrases: Convolutional Neural, Neural Network Architectures, Recurrent Neural Networks, deep learning, image analysis, networks

@booklet{EasyChair:12809,
  author    = {Roni Joni},
  title     = {Cutting-Edge Neural Network Architectures for Image Analysis: Harnessing Convolutional, Recurrent, and Generative Adversarial Networks in Deep Learning},
  howpublished = {EasyChair Preprint 12809},
  year      = {EasyChair, 2024}}