Auxiliary Task

Auxiliary tasks in machine learning involve training a model on a secondary, related task alongside the primary objective, aiming to improve the performance and generalization of the main task. Current research focuses on strategically designing auxiliary tasks to address specific challenges, such as data scarcity, imbalanced datasets, and distribution shifts, often employing techniques like contrastive learning, multi-task learning frameworks, and knowledge distillation. This approach has demonstrated significant improvements across diverse applications, including image processing, natural language processing, and reinforcement learning, highlighting its value for enhancing model robustness and efficiency.