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Related Questions
- What are the key differences between feature importance and saliency maps in image classification tasks?
- How do feature importance methods like Permutation Importance and SHAP values help in understanding the contribution of individual features in image classification models?
- Can you explain how saliency maps, such as Grad-CAM and CAM, are used to visualize the areas of the input image that contribute to the model's predictions in image classification tasks?
- What are some common applications of feature importance and saliency maps in image classification tasks, such as debugging and model interpretability?
- How can feature importance and saliency maps be used together to gain a deeper understanding of model behavior in image classification tasks?
- What are some best practices for interpreting feature importance and saliency maps in image classification tasks to avoid common pitfalls and misleading results?
- Can you provide examples of how feature importance and saliency maps have been used in real-world image classification tasks, such as medical image analysis or autonomous driving?
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