Improving AI using transfer and few-shot learning techniques

Transfer learning is a method that enables AI models to enhance their performance in one task by using the knowledge and abilities they acquired from another task. It aids in saving time and resources needed to train a new model from scratch by leveraging the existing models as a foundation. This accelerates the development process and also enables AI to be used in domains where data accessibility is scarce.