Transfer Risk

Transfer risk, the likelihood of a machine learning model's performance degrading when transferring knowledge from a source task to a new target task, is a critical area of research in transfer learning. Current efforts focus on developing reliable metrics to predict transferability, exploring diverse model architectures like diffusion models for generating adversarial examples to test robustness, and applying these concepts to diverse fields such as finance and healthcare. Understanding and mitigating transfer risk is crucial for improving the reliability and efficiency of transfer learning across various applications, enabling more robust and effective deployment of machine learning models in real-world scenarios.