is not only an active research topic, but also fascinating to commercial image search engines and photo-sharing websites since they would like to better capture data streams on the Internet and thus better serve user’s information need. Over the years, various techniques (click-through data) have been integrated to improve search engine results. Note that, using pretrained models (CNN [@xia2014well]) to clean up web data also falls into this category, since extensive human supervision has already been used.

**From Data to Models**: A more interesting and challenging direction is the opposite - can models automatically discover the hidden structures in the data and be trained directly from web data? Many people have pushed hard in this line of research. For example, earlier work focused on jointly modeling images and text and used text based search engines for gathering the data [@berg2006animals; @schroff2011harvesting; @saenko2009unsupervised]. This tends to offer less biased training pairs, but unfortunately such an association is often too weak and hard to capture, since visual knowledge is usually regarded as common sense knowledge and too obvious to be mentioned in the text [@chen2013neil]. As the image search engines became mature, recent work focused on using them to filter out the 