noise when learning visual models [@fergus2004visual; @wang2008annotating; @vijayanarasimhan2008keywords; @torresani2010efficient; @li2013harvesting; @divvala2014learning; @golge2014conceptmap]. But using image search engines added more bias to the gathered data  [@bergamo2010exploiting; @mezuman2012learning; @lin2014microsoft]. To combat both noise and data bias, recent approaches have taken a more semi-supervised approach. In particular, [@li2010optimol; @chen2013neil] proposed iterative approaches to jointly learn models and find clean examples, hoping that simple examples learned first can help the model learn harder, more complex examples [@bengio2009curriculum; @kumar2010self]. However, to the best of our knowledge, human supervision is still a clear winner in performance, regardless of orders of magnitudes more data seen by many of these web learners.

Our work is also closely related to another trend in computer vision: learning and exploiting visual representation via CNNs [@krizhevsky2012imagenet; @girshick2014rich; @taigman2014deepface; @hariharan2014simultaneous]. However, learning these CNNs from noisy labeled data [@sukhbaatar2014learning; @reed2014training] is still an open challenge. Following the recent success of convolutional networks and curriculum learning [@bengio2009curriculum; @kumar2010self; @lee2011learning], we demonstrate that, while directly training CNNs with high-level or fine-grained queries (random proper nouns, abstract concepts) and noisy labels (Flickr tags) can still be challenging, a more learning approach might provide us the right solution. Specifically, one can bootstrap CNN training with easy examples 