Task Similarity

Task similarity research explores how the relationships between different machine learning tasks impact model performance, aiming to improve knowledge transfer and mitigate catastrophic forgetting. Current efforts focus on developing algorithms and architectures that leverage task similarity for efficient continual learning and multi-task learning, often employing techniques like prompt tuning, optimal transport, and various regularization methods. This research is crucial for building more robust and efficient AI systems, particularly in scenarios with limited data or sequentially arriving tasks, impacting fields such as natural language processing, computer vision, and robotics. Understanding and effectively utilizing task similarity promises significant improvements in sample efficiency and generalization capabilities.