the BLVC reference network [@jia2014caffe], which is a slight variant of of the original network proposed by [@krizhevsky2012imagenet]. The architecture has five convolutional layers followed by two fully connected layers. After seventh layer, another fully connected layer is used to predict class labels.

Representation Adaptation with Graph
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After converging, the initial network has already learned favorable low-level filters to represent the “visual world” outlined by Google image search. However, as mentioned before, this “visual world” is biased toward clean and simple images. For example, it was found that more than 40% of the cars returned by Google are viewed from a 45 degree angle [@mezuman2012learning]. Moreover, when a concept is a product, lots of the images are wallpapers and advertisements with artificial background, with the product centered and pictured from the best selling view. On the other hand, photo-sharing websites like Flickr have more realistic images since the users upload their own photos. Though photographic bias still exists, most of the images are closer-looking to the visual world humans experience everyday. Datasets constructed from them are shown to generalize better [@torralba2011unbiased; @lin2014microsoft]. Therefore, as a next step, we aim to narrow the gap by fine-tuning our representation on Flickr 