to train a webly-supervised object detector we still need to clean the web data and localize the objects in those images to train a detector like R-CNN [@girshick2014rich]. Note that this is a non-trivial task, since: 1) the CNN is only trained to distinguish a closed set of classes, unnecessarily aware of all the negative visual world, background clutter; 2) the classification loss encourages the representation to be spatially *invariant* (, the network should output “orange” regardless of where it exists in the image or how many there are), which can be a serious issue for localization.

We now describe our subcategory discovery based approach similar to [@chen2013neil] to clean data and localize objects. The whole process is illustrated in Figure \[fig:loc\].

[**Seeds**]{}: We use the full images returned by Google as seed bounding boxes. This is based on Google’s bias toward images with a single centered object and a clean background.

[**Nearest Neighbor Propagation**]{}: For each seed, we train an Exemplar-LDA [@hariharan2012discriminative] detector using our trained $fc7$ features. Negative statistics for E-LDA are computed over all the downloaded images. This E-LDA detector is then fired on the remaining images to find its top $k$ nearest neighbors. For efficiency, instead 