Abstract
Learning from web data is increasingly popular due to
abundant free web resources. However, the performance
gap between webly supervised learning and traditional supervised learning is still very large, due to the label noise of
web data as well as the domain shift between web data and
test data. To fill this gap, most existing methods propose
to purify or augment web data using instance-level supervision, which generally requires heavy annotation. Instead,
we propose to address the label noise and domain shift by
using more accessible category-level supervision. In particular, we build our deep probabilistic framework upon variational autoencoder (VAE), in which classification network
and VAE can jointly leverage category-level hybrid information. Then, we extend our method for domain adaptation
followed by our low-rank refinement strategy. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed method