Grad-CAM++ is an improved method for generating visual explanations of deep convolutional networks (CNNs). It builds upon the Grad-CAM method, which provides visual explanations by highlighting regions of an image that are important for a CNN's prediction. Grad-CAM++ enhances this by using a weighted combination of positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score. This approach improves object localization and explains multiple object instances in a single image more effectively than state-of-the-art methods. The method is mathematically derived and computationally efficient, requiring only a single backward pass on the computational graph. Extensive experiments on standard datasets show that Grad-CAM++ provides human-interpretable visual explanations for various tasks, including classification, image caption generation, and 3D action recognition. It also performs well in new settings such as knowledge distillation, where it helps improve the performance of a student model by using explanations generated by a teacher model. Grad-CAM++ is evaluated using both objective and subjective metrics. Objective metrics include the average drop in confidence and the percentage increase in confidence when using only the explanation map. Subjective evaluations involve human studies where participants are asked to select the explanation that they find more trustworthy. Grad-CAM++ outperforms Grad-CAM in both types of evaluations, indicating that it provides more accurate and interpretable visual explanations. In addition to improving visual explanations, Grad-CAM++ is effective for object localization. It performs better than Grad-CAM in terms of the Intersection over Union (IoU) metric, which measures the overlap between the explanation map and the ground truth bounding box. Grad-CAM++ also shows improved performance in knowledge distillation, where it helps transfer knowledge from a teacher model to a student model. Grad-CAM++ is also applied to image captioning and 3D action recognition tasks. In image captioning, it generates more complete heatmaps that highlight the relevant parts of an image for the predicted caption. In 3D action recognition, it provides more semantically relevant explanations for the predicted action, highlighting the most discriminative parts of the video. Overall, Grad-CAM++ provides a more general and effective method for generating visual explanations of CNN predictions, improving both the interpretability and performance of deep learning models.Grad-CAM++ is an improved method for generating visual explanations of deep convolutional networks (CNNs). It builds upon the Grad-CAM method, which provides visual explanations by highlighting regions of an image that are important for a CNN's prediction. Grad-CAM++ enhances this by using a weighted combination of positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score. This approach improves object localization and explains multiple object instances in a single image more effectively than state-of-the-art methods. The method is mathematically derived and computationally efficient, requiring only a single backward pass on the computational graph. Extensive experiments on standard datasets show that Grad-CAM++ provides human-interpretable visual explanations for various tasks, including classification, image caption generation, and 3D action recognition. It also performs well in new settings such as knowledge distillation, where it helps improve the performance of a student model by using explanations generated by a teacher model. Grad-CAM++ is evaluated using both objective and subjective metrics. Objective metrics include the average drop in confidence and the percentage increase in confidence when using only the explanation map. Subjective evaluations involve human studies where participants are asked to select the explanation that they find more trustworthy. Grad-CAM++ outperforms Grad-CAM in both types of evaluations, indicating that it provides more accurate and interpretable visual explanations. In addition to improving visual explanations, Grad-CAM++ is effective for object localization. It performs better than Grad-CAM in terms of the Intersection over Union (IoU) metric, which measures the overlap between the explanation map and the ground truth bounding box. Grad-CAM++ also shows improved performance in knowledge distillation, where it helps transfer knowledge from a teacher model to a student model. Grad-CAM++ is also applied to image captioning and 3D action recognition tasks. In image captioning, it generates more complete heatmaps that highlight the relevant parts of an image for the predicted caption. In 3D action recognition, it provides more semantically relevant explanations for the predicted action, highlighting the most discriminative parts of the video. Overall, Grad-CAM++ provides a more general and effective method for generating visual explanations of CNN predictions, improving both the interpretability and performance of deep learning models.