Importance Map

Importance maps are visual representations highlighting the relative contribution of different input features to a model's decision-making process, enhancing the interpretability of complex models like those used in image classification and imitation learning. Current research focuses on developing methods to generate these maps, particularly for self-supervised models and in interactive settings, exploring their use in guiding human-AI collaboration and improving efficiency in tasks such as image compression and multi-agent pathfinding. The ability to effectively visualize and understand model decisions through importance maps is crucial for building trust, debugging, and improving the performance and usability of AI systems across various scientific and engineering domains.