Guided Backpropagation in Deep Neural Networks I am reading about a method called guided backpropagation. 
https://ramprs.github.io/2017/01/21/Grad-CAM-Making-Off-the-Shelf-Deep-Models-Transparent-through-Visual-Explanations.html#deconv-and-guided-backprop.

As shown in the figure below, you essentially compute the derivates of the output with respect to the input image while zero-ing out negative gradients along the way. The website mentions


  Negative gradients at a particular ReLU neuron, state that this neuron
  has a negative influence on the class that we are trying to visualize.
  
  During backpropagation there are paths that have positive influence
  and some that have negative influence, and these end up cancelling out
  in a weird interference pattern, causing gradients to seem noisy.
  Whereas in Guided Backpropagation, we only keep paths that lead to
  positive influence on the class score, and supress the ones that have
  negative influence, leading to much cleaner looking images.


My question is why does the resulting image have pixels which are negative? Don't negative pixels indicate that the gradient of the output with respect to the pixel is negative? I.e. that pixel has a negative influence on the class score?
Guided backpropagation from the model output to the input of the first ReLU indeed results in non-negative values (as the gradient from ReLU output wrt. ReLU input is set to zero if it is negative). From the input of the first ReLU the gradient wrt. the input pixels can however become negative. Input images are mostly scaled to the range [-1,1] (e.g. by division by 127.5 and subtraction of 1 for Keras' pretrained models - that means roughly achieving zero mean and unit variance). If a negative input (pixel that is darker than the mean) contributes positively - via negative weights - to the output of the first convolutional layer the gradient for that pixel is negative. As negative gradients are only set to zero in guided backprop when passing through a ReLU and there is no ReLU between the input of the first ReLU and the overall input, guided backprop can result in negative values.