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

  • Ramprasaath R. SelvarajuEmail author
  • Michael Cogswell
  • Abhishek Das
  • Ramakrishna Vedantam
  • Devi Parikh
  • Dhruv Batra


We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g.VGG), (2) CNNs used for structured outputs (e.g.captioning), (3) CNNs used in tasks with multi-modal inputs (e.g.visual question answering) or reinforcement learning, all without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at, along with a demo on CloudCV (Agrawal et al., in: Mobile cloud visual media computing, pp 265–290. Springer, 2015) ( and a video at


Grad-CAM Visual explanations Visualizations Explanations Interpretability Transparency 



This work was funded in part by NSF CAREER awards to DB and DP, DARPA XAI Grant to DB and DP, ONR YIP awards to DP and DB, ONR Grant N00014-14-1-0679 to DB, a Sloan Fellowship to DP, ARO YIP awards to DB and DP, an Allen Distinguished Investigator award to DP from the Paul G. Allen Family Foundation, ICTAS Junior Faculty awards to DB and DP, Google Faculty Research Awards to DP and DB, Amazon Academic Research Awards to DP and DB, AWS in Education Research Grant to DB, and NVIDIA GPU donations to DB. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government, or any sponsor. Funding was provided by Virginia Polytechnic Institute and State University.

Supplementary material


  1. Agrawal, A., Batra, D., & Parikh, D. (2016). Analyzing the behavior of visual question answering models. In EMNLP.Google Scholar
  2. Agrawal, H., Mathialagan, C. S., Goyal, Y., Chavali, N., Banik, P., Mohapatra, A., Osman, A., & Batra, D. (2015). CloudCV: Large scale distributed computer vision as a cloud service. In Mobile cloud visual media computing (pp. 265–290). Springer.Google Scholar
  3. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., & Parikh, D. (2015). VQA: Visual question answering. In ICCV.Google Scholar
  4. Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017). Network dissection: Quantifying interpretability of deep visual representations. In computer vision and pattern recognition.Google Scholar
  5. Bazzani, L., Bergamo, A., Anguelov, D., Torresani, L. (2016). Self-taught object localization with deep networks. In WACV.Google Scholar
  6. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.CrossRefGoogle Scholar
  7. Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., & Zitnick, C. L. (2015). Microsoft COCO captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325.
  8. Cinbis, R. G., Verbeek, J., & Schmid, C. (2016). Weakly supervised object localization with multi-fold multiple instance learning. IEEE transactions on pattern analysis and machine intelligence.Google Scholar
  9. Das, A., Agrawal, H., Zitnick, C. L., Parikh, D., & Batra, D. (2016). Human attention in visual question answering: Do humans and deep networks look at the same regions? In EMNLP.Google Scholar
  10. Das, A., Datta, S., Gkioxari, G., Lee, S., Parikh, D., & Batra, D. (2018). Embodied question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).Google Scholar
  11. Das, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J. M., Parikh, D., & Batra, D. (2017a). Visual dialog. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).Google Scholar
  12. Das, A., Kottur, S., Moura, J. M., Lee, S., & Batra, D. (2017b). Learning cooperative visual dialog agents with deep reinforcement learning. In Proceedings of the IEEE international conference on computer vision (ICCV).Google Scholar
  13. de Vries, H., Strub, F., Chandar, S., Pietquin, O., Larochelle, H., & Courville, A. C. (2017). Guesswhat?! visual object discovery through multi-modal dialogue. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).Google Scholar
  14. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In CVPR.Google Scholar
  15. Dosovitskiy, A., & Brox, T. (2015). Inverting convolutional networks with convolutional networks. In CVPR.Google Scholar
  16. Erhan, D., Bengio, Y., Courville, A., & Vincent, P. (2009). Visualizing higher-layer features of a deep network. University of Montreal, 1341.Google Scholar
  17. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2009). The PASCAL visual object classes challenge 2007 (VOC2007) results.
  18. Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015). From captions to visual concepts and back. In CVPR.Google Scholar
  19. Gan, C., Wang, N., Yang, Y., Yeung, D.-Y., & Hauptmann, A. G. (2015). Devnet: A deep event network for multimedia event detection and evidence recounting. In CVPR.Google Scholar
  20. Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., & Xu, W. (2015). Are you talking to a machine? In NIPS: Dataset and methods for multilingual image question answering.Google Scholar
  21. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR.Google Scholar
  22. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. stat.Google Scholar
  23. Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., & Farhadi, A. (2017). Iqa: Visual question answering in interactive environments. arXiv preprint arXiv:1712.03316.
  24. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR.Google Scholar
  25. Hoiem, D., Chodpathumwan, Y., & Dai, Q. (2012). Diagnosing error in object detectors. In ECCV.Google Scholar
  26. Jackson, P. (1998). Introduction to expert systems (3rd ed.). Boston, MA: Addison-Wesley Longman Publishing Co., Inc,zbMATHGoogle Scholar
  27. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In ACM MM.Google Scholar
  28. Johns, E., Mac Aodha, O., & Brostow, G. J. (2015). Becoming the expert—interactive multi-class machine teaching. In CVPR.Google Scholar
  29. Johnson, J., Karpathy, A., & Fei-Fei, L. (2016). DenseCap: Fully convolutional localization networks for dense captioning. In CVPR.Google Scholar
  30. Karpathy, A. (2014). What I learned from competing against a ConvNet on ImageNet.
  31. Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In CVPR.Google Scholar
  32. Kolesnikov, A., & Lampert, C. H. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV.Google Scholar
  33. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS.Google Scholar
  34. Lin, M., Chen, Q., & Yan, S. (2014a). Network in network. In ICLR.Google Scholar
  35. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014b). Microsoft coco: Common objects in context. In ECCV.Google Scholar
  36. Lipton, Z. C. (2016). The mythos of model interpretability. arXiv preprint arXiv:1606.03490v3.
  37. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In CVPR.Google Scholar
  38. Lu, J., Lin, X., Batra, D., & Parikh, D. (2015). Deeper LSTM and normalized CNN visual question answering model.
  39. Lu, J., Yang, J., Batra, D., & Parikh, D. (2016). Hierarchical question-image co-attention for visual question answering. In NIPS.Google Scholar
  40. Mahendran, A., & Vedaldi, A. (2016a). Salient deconvolutional networks. In European conference on computer vision.Google Scholar
  41. Mahendran, A., & Vedaldi, A. (2016b). Visualizing deep convolutional neural networks using natural pre-images. International Journal of Computer Vision, 1–23.Google Scholar
  42. Malinowski, M., Rohrbach, M., & Fritz, M. (2015). Ask your neurons: A neural-based approach to answering questions about images. In ICCV.Google Scholar
  43. Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014). Learning and transferring mid-level image representations using convolutional neural networks. In CVPR.Google Scholar
  44. Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015). Is object localization for free?—weakly-supervised learning with convolutional neural networks. In CVPR.Google Scholar
  45. Pinheiro, P. O., & Collobert, R. (2015). From image-level to pixel-level labeling with convolutional networks. In CVPR.Google Scholar
  46. Ren, M., Kiros, R., & Zemel, R. (2015). Exploring models and data for image question answering. In NIPS.Google Scholar
  47. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should i trust you?” Explaining the predictions of any classifier. In SIGKDD.Google Scholar
  48. Selvaraju, R. R., Chattopadhyay, P., Elhoseiny, M., Sharma, T., Batra, D., Parikh, D., & Lee, S. (2018). Choose your neuron: Incorporating domain knowledge through neuron-importance. In Proceedings of the European conference on computer vision (ECCV) (pp. 526–541).Google Scholar
  49. Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016). Grad-CAM: Why did you say that? Visual explanations from deep networks via gradient-based localization. CoRR. arXiv:1610.02391
  50. Selvaraju, R. R., Lee, S., Shen, Y., Jin, H., Ghosh, S., Heck, L., Batra, D., & Parikh, D. (2019) Taking a hint: Leveraging explanations to make vision and language models more grounded. In Proceedings of the international conference on computer vision (ICCV).Google Scholar
  51. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., et al. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489.CrossRefGoogle Scholar
  52. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In ICLR.Google Scholar
  53. Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. CoRR. arXiv:1312.6034
  54. Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. A. (2014). Striving for simplicity: The all convolutional net. CoRR. arXiv:1412.6806
  55. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826).Google Scholar
  56. Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and tell: A neural image caption generator. In CVPR.Google Scholar
  57. Vondrick, C., Khosla, A., Malisiewicz, T., & Torralba, A. (2013). HOGgles: Visualizing object detection features. ICCV.Google Scholar
  58. Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In ECCV.Google Scholar
  59. Zhang, J., Lin, Z., Brandt, J., Shen, X., & Sclaroff, S. (2016). Top-down neural attention by excitation backprop. In ECCV.Google Scholar
  60. Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. (2014). Object detectors emerge in deep scene cnns. CoRR. arXiv:1412.6856
  61. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In CVPR.Google Scholar
  62. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Ramprasaath R. Selvaraju
    • 1
    Email author
  • Michael Cogswell
    • 1
  • Abhishek Das
    • 1
  • Ramakrishna Vedantam
    • 1
  • Devi Parikh
    • 1
    • 2
  • Dhruv Batra
    • 1
    • 2
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.Facebook AI ResearchMenlo ParkUSA

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