There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction. Aditya Chattopadhyay, et al., "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Netw…

However, these deep models are perceived as ”black box” methods considering the lack of understanding of their internal functioning. Aditya Chattopadhyay, et al., "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks" h…

Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. Aditya Chattopadhyay, et al., "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks" https:…

Video of the demo can be found at youtu.be/COjUB9Izk6E. Ramprasaath R. Selvaraju, et al., "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization" https://arxiv.org/abs/1610.02391 出力のクラスに対応する判断根拠を…

Our code is available at https://github.com/ramprs/grad-cam/ and a demo is available on CloudCV. Ramprasaath R. Selvaraju, et al., "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization" https://arxiv.org/abs/161…

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 n…

For image captioning and VQA, our visualizations show even non-attention based models can localize inputs. Ramprasaath R. Selvaraju, et al., "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization" https://arxiv.o…

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) are robust to adversarial images, (c…