Augmented Grad-Cam: Heat-Maps Super Resolution Through Augmentation

We present Augmented Grad-CAM, a general framework to provide a high-resolution visual explanation of CNN outputs. Our idea is to take advantage of image augmentation to aggregate multiple low-resolution heat-maps -- in our experiments Grad-CAMs -- computed from augmented copies of the same input image. We generate the high-resolution heat-map through super-resolution, and we formulate a general optimization problem based on Total Variation regularization. This problem is entirely solved on the GPU at inference time, together with image augmentation. Augmented Grad-CAM outperforms Grad-CAM in weakly supervised localization on Imagenet dataset, and provides more detailed heat-maps. Moreover, Augmented Grad-CAM turns to be particularly useful in monitoring the production of silicon wafers, where CNNs are employed to classify defective patterns on the wafer surface to detect harmful faults in the production line.