View article

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. 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 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 (eg VGG),(2) CNNs used for structured outputs (eg captioning),(3) CNNs used in tasks with multi-modal inputs (eg VQA) or reinforcement learning, and needs no architectural changes or re-training. 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 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 more faithful to the underlying model, and (d) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models can localize inputs. Finally, we design and conduct human studies to measure if Grad-CAM …