Vol. 4, Issue 10, Part A (2018)

Transfer learning involves leveraging knowledge gained from a source task to improve performance on a target task. In the context of deep learning, pre-trained models on large datasets, such as ImageNet, have demonstrated remarkable capabilities in capturing generic features. These features can be transferred and fine-tuned for specific tasks, allowing models to learn more efficiently with limited labeled data. This approach is particularly beneficial when working with domains where acquiring extensive labeled datasets is challenging or expensive.

This review paper explores the pivotal role of transfer learning in mitigating overfitting and enhancing the generalization of deep learning models. We delve into various transfer learning strategies, including feature extraction, fine-tuning, and domain adaptation, and examine their effectiveness across diverse domains such as computer vision, natural language processing, and speech recognition. Additionally, we discuss the impact of different pre-training architectures and the transferability of learned representations between tasks.

Furthermore, the paper investigates the challenges and limitations associated with transfer learning, such as domain misalignment and task dissimilarity. We analyze ongoing research efforts aimed at addressing these challenges and improving the adaptability of transfer learning methods. Additionally, we highlight recent advancements, such as meta-learning and self-supervised learning, which contribute to the continual evolution of transfer learning techniques.