performance fall compared to a CNN trained with November 2014 data on the same categories. This indicates that the underlying CNN in Google image search has minimal effect on the training procedure and our network is quite robust to noise.

Object Localization\[sec:subcat\]
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In this subsection, we are interested to see if we can detect objects without using a single PASCAL training image. We believe this is possible since we can localize objects automatically in web images with our proposed approach (see Section \[sec:loc\]). Please refer to Figure \[fig:subcat\] for the qualitative results on the training localization we can get with $fc7$ features. Compared to [@chen2013neil], the subcategories we get are less homogeneous (people are not well-aligned, objects in different view points are clustered together). But just because of this more powerful representation (and thus better distance metric), we are able to dig out more signal from the training set - since semantically related images can form clusters and won’t be purged as noise when an image is evaluated by its nearest neighbors.

Using localized objects, we train R-CNN based detectors to detect objects on the VOC 2007 test set. We compare our results against [@divvala2014learning], who used Google $n$-grams 