Saliency Maps

Saliency maps are visual representations that highlight the most important regions in an image for a given task, typically used in the context of computer vision. They are generated by analyzing the output of neural networks to determine which parts of an image contribute most significantly to the model's predictions. Common characteristics include the ability to indicate areas of high relevance, often using color gradients to show varying levels of importance. Saliency maps are widely used in applications such as image classification, object detection, and understanding model behavior, helping researchers and developers interpret how models make decisions based on visual inputs.