Proxy Task

Proxy tasks in machine learning involve training a model on a related, simpler task to improve performance on a more complex primary task. Current research focuses on applications like improving robustness to adversarial attacks, handling domain shifts at test time, and mitigating privacy concerns in data annotation. This approach is being explored across various model architectures, including neural networks and large language models, with applications ranging from medical image analysis to object detection in challenging environments. The effectiveness of proxy tasks lies in their ability to enhance model generalization, efficiency, and data utilization, leading to improved accuracy and robustness in diverse applications.