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Related Questions
- How do CAM and Grad-CAM methods visualize the importance of different features in a deep neural network's decision-making process?
- What are some common use cases for CAM and Grad-CAM in computer vision tasks, such as image classification and object detection?
- Can CAM and Grad-CAM be applied to other types of deep learning models, such as natural language processing or reinforcement learning?
- How do CAM and Grad-CAM compare to other techniques for interpreting deep neural network decisions, such as saliency maps or feature importance scores?
- What are some challenges and limitations of using CAM and Grad-CAM, and how can they be addressed?
- Can CAM and Grad-CAM be used to identify biases or errors in deep neural network decisions?
- How can CAM and Grad-CAM be used to improve the transparency and accountability of deep neural network decision-making processes?
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