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…

We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResN…

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 in…

Our approach – Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say logits for ‘dog’ or even a caption), flowing into the final convolutional layer to produce a coarse localization map highli…

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. Ramprasaath R. Selvaraju, et al., "Grad-CAM: Visual Explanations …