Deep Learning for Image Classification: Methods, Challenges, and Future Directions
DOI:
https://doi.org/10.32628/CSEIT2511110Keywords:
Deep Learning, Image Classification, Convolutional Neural Networks (CNNs), Transfer Learning, Vision Transformers (ViTs), Data Augmentation, Interpretability, Few-Shot Learning, Self-Supervised Learning, Multimodal Learning, Federated LearningAbstract
Image classification has been fundamentally changed by deep learning that has driven unprecedented accuracy and has empowered applications ranging from healthcare to autonomous cars to security. For example, medical imaging has been diagnosed for diseases such as diabetic retinopathy and tumour detection using deep learning models to an excellent degree. Object classification algorithms in autonomous vehicles are responsible for enabling real time navigation and obstacle avoidance. More recently, the advances in image classification have been made possible with recent breakthroughs including Vision Transformers (ViTs) and self-supervised learning models like SimCLR. In this paper, we explore the main methods on which the deep learning-based image classification fundamentally lies, including the convolutional neural networks (CNNs), transfer learning, and attention mechanisms. Finally, it also discusses the field challenges, like the need to large labelled datasets, computational requirements, and interpretability and it provides solutions to overcome them. We conclude with promising future directions including few shots learning, unsupervised learning and the combination of multimodal data and how they will further advance and open up new applications.
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Jagdish Jangid , " Efficient Training Data Caching for Deep Learning in Edge Computing Networks" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 5, pp.337-362, September-October-2020. Available at doi : https://doi.org/10.32628/CSEIT20631113
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