Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks (1710.11063v3)

Abstract: Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning. There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction. Building on a recently proposed method called Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide better visual explanations of CNN model predictions, in terms of better object localization as well as explaining occurrences of multiple object instances in a single image, when compared to state-of-the-art. We provide a mathematical derivation for the proposed method, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate a visual explanation for the corresponding class label. Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ provides promising human-interpretable visual explanations for a given CNN architecture across multiple tasks including classification, image caption generation and 3D action recognition; as well as in new settings such as knowledge distillation.

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

The paper "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks" introduces Grad-CAM++, a novel method to enhance the explainability of Convolutional Neural Networks (CNNs). This work extends the capabilities of the existing Grad-CAM technique by addressing its limitations in object localization and handling multiple object instances within an image.

Overview of Grad-CAM++

The primary contribution of this paper is the enhancement of visual explanations generated by Grad-CAM for deep CNNs. Grad-CAM++ leverages the positive partial derivatives of the output with respect to the feature maps from the last convolutional layer. It introduces pixel-wise weighting of these gradients to generate more precise and comprehensive saliency maps. This nuanced approach ensures better object localization and resolution of multiple instances of the same class within an image, which were challenging for the original Grad-CAM.

Methodological Innovations

The authors derive closed-form solutions for the pixel-wise weights, which ensures that the computational overhead remains comparable to that of Grad-CAM. Unlike Grad-CAM, which uses a global averaging approach for gradient weights, Grad-CAM++ computes pixel-wise weights for more detailed saliency maps, leading to better visual resolutions. This method only requires a single backward pass through the computational graph, making it computationally efficient. The authors also provide exact expressions for higher-order derivatives for both softmax and exponential output activation functions, enhancing the method's generalizability.

Evaluation and Results

The paper rigorously evaluates Grad-CAM++ on standard datasets such as ImageNet and Pascal VOC 2007. The evaluation metrics include:

• Average Drop \%: Measures the drop in the model's confidence when the explanation map region is provided as input. Grad-CAM++ demonstrated a lower average drop percentage compared to Grad-CAM, indicating better preservation of confidence.
• % Increase in Confidence: Captures the cases where showing only the explanation map region increases the model's confidence. Grad-CAM++ showed a higher percentage increase, suggesting more relevant information retention.
• Win \%: Compares instances where Grad-CAM++ explanations had a less significant decrease in confidence than Grad-CAM. Grad-CAM++ achieved a higher win percentage, reinforcing its superior performance.

The paper also includes comprehensive human evaluations to assess the trust and interpretability of the explanations. Participants consistently rated Grad-CAM++ explanations as more trustworthy and accurate than those generated by Grad-CAM.

Applications Beyond Object Recognition

In addition to object recognition, Grad-CAM++ was tested on image captioning and 3D action recognition tasks. For image captioning, Grad-CAM++ produced more complete and relevant visual explanations aligning well with the predicted captions. In 3D action recognition, Grad-CAM++ outperformed Grad-CAM in generating coherent explanation mappings for video frames, offering better insights into model decisions over time.

Implications and Future Directions

The paper's contributions imply significant advancements in making CNNs more interpretable and trustworthy, which is crucial for applications in security, healthcare, and autonomous systems. The refined object localization and handling of multiple object instances by Grad-CAM++ pave the way for more transparent AI models.

The future directions suggested include the exploration of explainable AI in multitask scenarios, improving the fidelity of Grad-CAM++ for recurrent neural networks, and extending the methodology to other deep learning architectures like Generative Adversarial Networks (GANs). Further research can also delve into the use of explanation-based learning to enhance knowledge distillation in teacher-student networks, as preliminary experiments have shown promise in improving student model performance.

Conclusion

Grad-CAM++ represents a significant step toward more interpretable and explainable CNNs, overcoming some of the critical limitations of Grad-CAM. By enabling better visualizations that align closely with model decisions, it enhances both human trust and model transparency. The paper's comprehensive analysis and experimental validation firmly establish Grad-CAM++ as a valuable tool in the domain of explainable AI.