GradCAM, which stands for Gradient-weighted Class Activation Mapping, is a technique in machine learning and computer vision used to visualize the areas of an input image that contributed the most to the final decision made by a Convolutional Neural Network (CNN). It works by creating a heat-map to visualize the parts of an image that contributed to a model's prediction, using the gradients of any target concept flowing into the final convolutional layer to create a coarse localization map. This map highlights important regions in the input image that are significant for predicting the target concept and can be used to understand what the model is focusing on when making a prediction. Grad-CAM is more versatile than previous approaches, allowing it to be applicable to a wide variety of CNN model types without necessitating architectural changes or re-training. Additionally, it can be combined with fine-grained visualizations from methods like Guided Backpropagation to create Guided Grad-CAM, a visualization that is high-resolution and class-discriminative, making it more informative and interpretable. Overall, Grad-CAM is a crucial tool for debugging deep learning models and ensuring they are performing as expected.

What is GradCam in machine learning?

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