no code implementations • 19 Nov 2018 • Li Niu, Ashok Veeraraghavan, Ashu Sabharwal
In the extreme case, given a set of test categories without any well-labeled training data, the majority of existing works can be grouped into the following two research directions: 1) crawl noisy labeled web data for the test categories as training data, which is dubbed as webly supervised learning; 2) transfer the knowledge from auxiliary categories with well-labeled training data to the test categories, which corresponds to zero-shot learning setting.
no code implementations • CVPR 2018 • Li Niu, Qingtao Tang, Ashok Veeraraghavan, Ashu Sabharwal
As tons of photos are being uploaded to public websites (e. g., Flickr, Bing, and Google) every day, learning from web data has become an increasingly popular research direction because of freely available web resources, which is also referred to as webly supervised learning.