Relevance Map

Relevance maps are visual representations highlighting the importance of different input features in a model's decision-making process, aiming to improve model interpretability and performance. Current research focuses on developing and refining relevance map generation methods within various model architectures, including deep neural networks, transformers, and specific algorithms like Layer-wise Relevance Propagation (LRP), often applied to tasks such as image and audio classification, ad relevance, and information retrieval. These advancements enhance model explainability, enabling better understanding of model biases and facilitating improvements in robustness and efficiency, with significant implications for diverse fields including e-commerce, advertising, and AI safety.