Transfer Learning in Neural Networks: Enhancing Model Efficiency and Performance
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Abstract
One of the most effective deep learning techniques, transfer learning allows models to take what they have learned from one task and apply it to others that are similar. When working with limited labeled data or computational resources, this method greatly improves model efficiency and performance. Training time, generalization, and the requirement for big annotated datasets are all reduced by transfer learning, which involves reusing pre-trained models and fine-tuning them for particular applications. domain adaptation, feature extraction, and fine-tuning are all cornerstones of neural network transfer learning. It looks at how tasks like image classification, NLP, and speech recognition can benefit from cross-domain knowledge transfer in terms of learning speed and prediction accuracy. Additionally, the paper discusses popular pre-trained models and architectures, elaborating on how they facilitate efficient and scalable AI solutions. the benefits and drawbacks of transfer learning, such as problems with overfitting models, negative transfer, and domain mismatch. It delves deeper into real-world applications in several fields, showing how computer vision, intelligent systems, and healthcare diagnostics have all benefited from transfer learning.
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