Advancing Deep Learning Models through Robust Feature Extraction and Transfer Learning for Enhanced Image Recognition

Deep learning has changed image recognition forever, which has achieved amazing results in many areas. On the other hand, deep learning models depend a lot on the quality and amount of labeled training data. This study suggests a new way to improve deep learning models for image recognition tasks using transfer learning and strong feature extraction methods. First, we look into how well different feature extraction methods, like autoencoders and convolutional neural networks (CNNs), can distinguish features from pictures. Considering different network structures and pre-training methods, we look at how well they do in terms of accuracy and how quickly they can do their work. Next, we look at transfer learning as using models already trained on big datasets to make deep learning models better at generalization. We look into various transfer learning methods, such as fine-tuning, feature extraction, and domain adaptation, to make the already-trained models work for new picture recognition tasks. We also deal with the domain shift problem by suggesting ways to match the source and target domains' feature distributions. We look into methods like domain adaptation and antagonistic training to reduce the difference in distribution and make the learned features more transferable. We do a lot of tests on standard image recognition datasets like ImageNet and RobustFeatureNet to see how well the proposed method works. Our approach greatly enhances the performance of deep learning models compared to traditional training methods, as shown by the testing results. The suggested framework gets the highest level of accuracy while lowering the need for a large labeled dataset.