In spite of the discouraging sanity check results for Guided Backpropagation (which by extension is also discouraging for DeconvNets, as we shall see that the methodology for DeconvNets is overlaps that of Guided Backpropagation), I am still writing about DeconvNets and Guided Backpropagation in this post for several reasons: Specifically, “Gradients” passes Adebayo et al.’s sanity checks, DeconvNets were not tested, and Guided Backpropagation fails the sanity checks. which suggests that out of these three popular methods, only “Gradients” is effective. 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. 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. ![]() All three of the methods discussed in this post are a form of post-hoc attention, which is different from trainable attention. 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. This post summarizes three closely related methods for creating saliency maps: Gradients (2013), DeconvNets (2014), and Guided Backpropagation (2014).
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