scene recognition, using this technique, indicated that activations are related to objects. Thus it lead to the conclusion that multiple object detectors are incorporated in such a deep architecture [@Zhou:15]. This means that high activations were triggered simultaneously in multiple parts of an image, being thus rather spatially vague. In parallel, visualization of activations for genre [@Tan:16] have shown that, for instance, the landscape type of scene leads to activating almost the entire image, thus being less neat to draw any conclusion. Consequently, we tried a different approach to investigate the intrinsic mechanisms of deep CNNs. Our approach exploits domain transfer and simply correlates the final results with the training database.

Given the increased power of machine learning systems and the limited amount of data available to a specific task, a plethora of transfer learning techniques appeared [@Lu2015]. Transfer learning is particularly popular when associated with deep learning. First, let us recall that the lower layers of deep nets trained on large databases are extremely powerful features when coupled with a powerful classifier (such as SVM) and may act as feature selectors, no matter the task [@donahue2014]. Secondly, the process of fine tuning deep networks assumes using a network that 