2000 character limit reached
Enhancing Explainable AI: A Hybrid Approach Combining GradCAM and LRP for CNN Interpretability (2405.12175v1)
Published 20 May 2024 in cs.CV
Abstract: 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.
- R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618–626.
- B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” 2015.
- S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,” PLOS ONE, vol. 10, no. 7, p. e0130140, 2015.
- G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller, “Explaining nonlinear classification decisions with deep taylor decomposition,” Pattern Recognition, vol. 65, pp. 211–222, 2017.
- A. Shrikumar, P. Greenside, A. Shcherbina, and A. Kundaje, “Learning important features through propagating activation differences,” in Proceedings of the 34th International Conference on Machine Learning, vol. 70. PMLR, 2017, pp. 3145–3153.
- M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should i trust you? explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016, pp. 1135–1144.
- M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European Conference on Computer Vision (ECCV). Springer, 2014, pp. 818–833.
- S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS). Curran Associates, Inc., 2017, pp. 4765–4774.
- J. R. Lee, S. Kim, I. Park, T. Eo, and D. Hwang, “Relevance-cam: Your model already knows where to look,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14 939–14 948.
- B. K. Iwana, R. Kuroki, and S. Uchida, “Explaining convolutional neural networks using softmax gradient layer-wise relevance propagation,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019, pp. 4176–4185.
- C. Qiu, F. Jin, and Y. Zhang, “Empowering cam-based methods with capability to generate fine-grained and high-faithfulness explanations,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 5, pp. 4587–4595, Mar. 2024. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/28258
- A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian, “Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 839–847.
- J. Gildenblat and contributors, “Pytorch library for cam methods,” https://github.com/jacobgil/pytorch-grad-cam, 2021.
- M. Kohlbrenner, A. Bauer, S. Nakajima, A. Binder, W. Samek, and S. Lapuschkin, “Towards best practice in explaining neural network decisions with lrp,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–7.
- N. Kokhlikyan, V. Miglani, M. Martin, E. Wang, B. Alsallakh, J. Reynolds, A. Melnikov, N. Kliushkina, C. Araya, S. Yan, and O. Reblitz-Richardson, “Captum: A unified and generic model interpretability library for pytorch,” 2020.
- C.-K. Yeh, C.-Y. Hsieh, A. S. Suggala, D. I. Inouye, and P. Ravikumar, “On the (in)fidelity and sensitivity of explanations,” in Neural Information Processing Systems (NeurIPS), dec 2019.
- U. Bhatt, A. Weller, and J. M. F. Moura, “Evaluating and aggregating feature-based model explanations,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, ser. IJCAI’20, 2021.
- P. Chalasani, J. Chen, A. R. Chowdhury, X. Wu, and S. Jha, “Concise explanations of neural networks using adversarial training,” in Proceedings of the 37th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 1383–1391. [Online]. Available: https://proceedings.mlr.press/v119/chalasani20a.html
- L. Arras, A. Osman, and W. Samek, “Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations,” Information Fusion, vol. 81, pp. 14–40, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1566253521002335
- L. Sixt, M. Granz, and T. Landgraf, “When explanations lie: Why many modified BP attributions fail,” in Proceedings of the 37th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 9046–9057. [Online]. Available: https://proceedings.mlr.press/v119/sixt20a.html