have looked at different aspects of webly supervised learning such as:

-   [**What are the good sources of data?**]{} Researchers have tried various search engines ranging from text/image search engines [@berg2006animals; @wang2008annotating; @vijayanarasimhan2008keywords; @fergus2010learning] to Flickr images [@ordonez2011im2text].

-   [**What types of data can be exploited?**]{} Researchers have tried to explore different types of data, like images-only [@li2010optimol; @chen2013neil], images-with-text [@berg2006animals; @schroff2011harvesting] or even images-with-$n$-grams [@divvala2014learning]).

-   [**How do we exploit the data?**]{} Extensive algorithms (probabilistic models [@fergus2010learning; @li2010optimol], exemplar based models [@chen2013neil], deformable part models [@divvala2014learning], self organizing map [@golge2014conceptmap] ) have been developed.

-   [**What should we learn from web data?**]{} There has been lot of effort ranging from just cleaning data [@fan2010harvesting; @xia2014well; @ordonez2011im2text] to training visual models [@li2010optimol; @torresani2010efficient; @li2013harvesting], to even discovering common-sense relationships [@chen2013neil].

Nevertheless, while many of these systems have seen orders of magnitudes larger number of images, their performance has never matched up against contemporary methods that receive extensive supervision from humans. Why is that?

Of course the biggest issue is the data itself: 1) it contains noise, and 2) is has bias - image search engines like Google usually operate in the high-precision low-recall regime and tend to be biased toward 