VOC [@everingham2010pascal] and MS COCO [@lin2014microsoft] have been created from Google or Flickr by harnessing human intelligence to filter out the noisy images and label object locations. The resulting clean data has helped significantly advance performance on relevant tasks [@lsvm-pami; @krizhevsky2012imagenet; @girshick2014rich; @zhou2014learning]. For example, training a neural network on ImageNet followed by fine-tuning on PASCAL VOC has led to the state-of-the-art performance on the object detection challenge [@krizhevsky2012imagenet; @girshick2014rich]. But human supervision comes with a cost and its own problems (inconsistency, incompleteness and bias [@torralba2011unbiased]). Therefore, an alternative, and more appealing way is to learn visual representations and object detectors from the web data directly, without using any manual labeling of bounding boxes. But the big question is, can we actually use millions of images online without using any human supervision?

![[We investigate the problem of training a webly supervised CNN. Two types of visual data are available online: image search engine results (left) and photo-sharing websites (right). We train a two-stage network bootstrapping from clean examples retrieved by Google, and enhanced by noisier images from Flickr.]{}\[fig:teaser\]](teaser2){width="0.97\linewidth"}

In fact, researchers have pushed hard to realize this dream of learning visual representations and object detectors from web data. These efforts 