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• We show that saliency maps can be obtained as a byproduct of image classification. • Our method does not require any saliency ground truth. • We include an extensive study of the effect of center bias on the results. • We get competitive results even when comparing to methods that do use ground truth. Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods. [ABSTRACT FROM AUTHOR]