that CNNs can be readily trained for easy categories using images retrieved by search engines. We then adapt this network to hard (Flickr style) web images using the relationships discovered in easy images. 2) We show webly supervised CNNs also generalize well to relevant vision tasks, giving state-of-the-art performance compared to ImageNet pretrained CNNs if there is enough data. 3) We show state-of-the-art performance on VOC data for the scenario where not a single VOC training image is used - just the images from the web. 4) We also show competitive results on scene classification. We believe this paper opens up avenues for exploitation of web data to achieve next cycle of performance gain in vision tasks (and at no human labeling costs!).

Why Webly Supervised?
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Driven by CNNs, the field of object detection has seen a dramatic churning in the past two years, which has resulted in a significant improvement in the state-of-the-art performance. But as we move forward, how do we further improve performance of CNN-based approaches? We believe there are two directions. The first and already explored area is designing deeper networks [@simonyan2014very; @szegedy2014going]. We believe a more promising direction is to feed more data into 