Efficient Transfer

Efficient transfer learning aims to leverage knowledge gained from solving one task to improve performance on a related but different task, minimizing the need for extensive retraining. Current research focuses on improving sample efficiency and few-shot learning capabilities, employing techniques like pre-trained models (e.g., EfficientNet), reward machine abstractions in reinforcement learning, and diverse ensemble methods. These advancements are crucial for addressing data scarcity in various domains, such as bioacoustic classification and material defect detection, leading to more efficient and robust machine learning applications.

Papers