Improving Transferability

Improving transferability in machine learning focuses on enhancing the ability of models trained on one dataset (source) to perform well on a different, related dataset (target), minimizing the need for retraining. Current research emphasizes methods for selecting informative source data, developing more robust and efficient adversarial attacks, and designing model architectures and training strategies that promote generalization across domains. These advancements are crucial for addressing data scarcity in many applications, such as medical image analysis and network security, and for building more robust and reliable AI systems.