Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization

Articles citing Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization

Title / Author	Year
Recent advances in convolutional neural networks J Gu, Z Wang, J Kuen, L Ma, A Shahroudy, B Shuai, T Liu, X Wang, ... Pattern Recognition 77, 354-377	2018
Knowing when to look: Adaptive attention via a visual sentinel for image captioning J Lu, C Xiong, D Parikh, R Socher Proceedings of the IEEE conference on computer vision and pattern …	2017
Methods for interpreting and understanding deep neural networks G Montavon, W Samek, KR Müller Digital Signal Processing 73, 1-15	2018
Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering Y Goyal, T Khot, D Summers-Stay, D Batra, D Parikh Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of The Royal Society Interface 15 (141), 20170387	2018
Learning important features through propagating activation differences A Shrikumar, P Greenside, A Kundaje Proceedings of the 34th International Conference on Machine Learning-Volume …	2017
Thinet: A filter level pruning method for deep neural network compression JH Luo, J Wu, W Lin Proceedings of the IEEE international conference on computer vision, 5058-5066	2017
Interpretable explanations of black boxes by meaningful perturbation RC Fong, A Vedaldi Proceedings of the IEEE International Conference on Computer Vision, 3429-3437	2017
A survey of methods for explaining black box models R Guidotti, A Monreale, S Ruggieri, F Turini, F Giannotti, D Pedreschi ACM computing surveys (CSUR) 51 (5), 93	2018
Human attention in visual question answering: Do humans and deep networks look at the same regions? A Das, H Agrawal, L Zitnick, D Parikh, D Batra Computer Vision and Image Understanding 163, 90-100	2017
Adversarial examples: Attacks and defenses for deep learning X Yuan, P He, Q Zhu, X Li IEEE transactions on neural networks and learning systems	2019
The building blocks of interpretability C Olah, A Satyanarayan, I Johnson, S Carter, L Schubert, K Ye, ... Distill 3 (3), e10	2018
Cbam: Convolutional block attention module S Woo, J Park, JY Lee, I So Kweon Proceedings of the European Conference on Computer Vision (ECCV), 3-19	2018
Interpretable convolutional neural networks Q Zhang, Y Nian Wu, SC Zhu Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Visual interpretability for deep learning: a survey Q Zhang, SC Zhu Frontiers of Information Technology & Electronic Engineering 19 (1), 27-39	2018
Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda A Abdul, J Vermeulen, D Wang, BY Lim, M Kankanhalli Proceedings of the 2018 CHI conference on human factors in computing systems …	2018
Visual analytics in deep learning: An interrogative survey for the next frontiers FM Hohman, M Kahng, R Pienta, DH Chau IEEE transactions on visualization and computer graphics	2018
Accountability of AI under the law: The role of explanation F Doshi-Velez, M Kortz, R Budish, C Bavitz, S Gershman, D O'Brien, ... arXiv preprint arXiv:1711.01134	2017
Multimodal explanations: Justifying decisions and pointing to the evidence D Huk Park, L Anne Hendricks, Z Akata, A Rohrbach, B Schiele, T Darrell, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Dronet: Learning to fly by driving A Loquercio, AI Maqueda, CR Del-Blanco, D Scaramuzza IEEE Robotics and Automation Letters 3 (2), 1088-1095	2018

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