Transfer learning enables efficient adaptation of pre-trained AI models for specific tasks, reducing development time and resource needs while improving performance with limited data. It involves selecting a suitable model, modifying its architecture, and retraining it on new data. Key types include inductive, transductive, and unsupervised transfer learning, along with few-shot and zero-shot learning. Benefits include quicker deployment and robustness; challenges involve negative transfer, domain mismatch, and model selection. Applications span computer vision, natural language processing, and speech recognition.
https://www.grammarly.com/blog/ai/what-is-transfer-learning/