CNN Heat Maps: Gradients vs. DeconvNets vs. Guided Backpropagation - WebSystemer.no

This post summarizes three closely related methods for creating saliency maps: Gradients (2013), DeconvNets (2014), and Guided Backpropagation (2014). Saliency maps are heat maps that are intended to provide insight into what aspects of an input image a convolutional neural network is using to make a prediction. All three of the methods discussed in this post are a form of post-hoc attention, which is different from trainable attention. Although in the original papers these methods are described in different ways, it turns out that they are all identical except for the way that they handle backpropagation through the ReLU nonlinearity. Please stay tuned for the next post, "CNN Heat Maps: Sanity Checks for Saliency Maps" for a discussion of a 2018 paper by Adebayo et al. which suggests that out of these three popular methods, only "Gradients" is effective.