  The presence of a bias in each image data collection has recently attracted a
lot of attention in the computer vision community showing the limits in
generalization of any learning method trained on a specific dataset. At the
same time, with the rapid development of deep learning architectures, the
activation values of Convolutional Neural Networks (CNN) are emerging as
reliable and robust image descriptors. In this paper we propose to verify the
potential of the DeCAF features when facing the dataset bias problem. We
conduct a series of analyses looking at how existing datasets differ among each
other and verifying the performance of existing debiasing methods under
different representations. We learn important lessons on which part of the
dataset bias problem can be considered solved and which open questions still
need to be tackled.
