Guided Backpropagation in Deep Neural Networks
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.