  Convolutional networks trained on large supervised dataset produce visual
features which form the basis for the state-of-the-art in many computer-vision
problems. Further improvements of these visual features will likely require
even larger manually labeled data sets, which severely limits the pace at which
progress can be made. In this paper, we explore the potential of leveraging
massive, weakly-labeled image collections for learning good visual features. We
train convolutional networks on a dataset of 100 million Flickr photos and
captions, and show that these networks produce features that perform well in a
range of vision problems. We also show that the networks appropriately capture
word similarity, and learn correspondences between different languages.
