Attention Maps
Attention maps are visual representations that highlight which parts of an input (such as an image or text sequence) a neural network model focuses on when making predictions, commonly used in computer vision and natural language processing. They provide interpretability by showing the 'attention' weights assigned to different regions or tokens, helping to understand model decisions and identify potential biases. Techniques like Grad-CAM, self-attention visualization, and saliency maps are popular methods for generating these visualizations.
Developers should learn about attention maps when working with deep learning models, especially in domains requiring model interpretability, such as medical imaging, autonomous vehicles, or ethical AI, to debug and validate model behavior. They are crucial for explaining predictions to stakeholders, ensuring fairness, and improving model performance by identifying misaligned focus areas, such as in image classification or machine translation tasks.