box, we add it for training. [**Category Expansion (CE)**]{}: One big advantage of Internet is its nearly infinite data limit. Here we again use the relationship graph to look for similar categories for more training examples. After verification the semantic-relatedness with WordNet [@miller1995wordnet], we add the examples into training dataset. We believe the extra examples should allow better generalization. Note both these strategies are only used to increase the amount of positive data for the final SVM to be trained in R-CNN. We do not re-train our CNN representations using these strategies.

Experimental Results\[sec:exp\]
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We now describe our experimental results. Our goal is to demonstrate that the visual representation learned using two-step webly supervised learning is meaningful. For this, we will do four experiments: 1) First, we will show that our learned CNN can be used for object detection. Here, we use the approach similar to R-CNN [@girshick2014rich] where we will fine-tune our learned CNN using VOC data. This is followed by learning SVM-detectors using CNN features. 2) We will also show that our CNN can be used to clean up the web data: that is, discover subcategories and localize the objects in web images. 3) We will train 