Heatmap Based Method

Heatmap-based methods are increasingly used to visualize and interpret the decision-making processes of deep neural networks, particularly in computer vision and related fields. Current research focuses on improving the accuracy and efficiency of heatmap generation, exploring various algorithms like guided backpropagation and integrated gradients, and addressing challenges such as quantization errors and computational cost. These methods are significant for enhancing model explainability, improving the reliability of predictions (e.g., in medical image analysis), and guiding the development of more robust and effective AI systems across diverse applications. Furthermore, research is exploring the integration of heatmaps with other techniques, such as large language models, to provide more comprehensive and accessible explanations.