Enhancing Explainable AI: A Hybrid Approach Combining GradCAM and LRP for CNN Interpretability
This is an incremental improvement for researchers and practitioners needing more interpretable CNN explanations in AI applications.
The paper tackles the problem of explaining CNN model outputs by developing a hybrid method that combines GradCAM and LRP with noise removal and Gaussian blur processing. The result shows the method performs better on Complexity than both baseline methods and outperforms at least one of them in other metrics like Faithfulness, Robustness, Localisation, and Randomisation.
We present a new technique that explains the output of a CNN-based model using a combination of GradCAM and LRP methods. Both of these methods produce visual explanations by highlighting input regions that are important for predictions. In the new method, the explanation produced by GradCAM is first processed to remove noises. The processed output is then multiplied elementwise with the output of LRP. Finally, a Gaussian blur is applied on the product. We compared the proposed method with GradCAM and LRP on the metrics of Faithfulness, Robustness, Complexity, Localisation and Randomisation. It was observed that this method performs better on Complexity than both GradCAM and LRP and is better than atleast one of them in the other metrics.