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. In order to cope with label noise with category-level supervision, we opt for autoencoder-like network structure, because autoencoder has been used for outlier detection [37, 46] and its hidden layer can be regulated by prior information, which is suitable for our task. Specifically, we build our probabilistic framework upon a probabilistic variant of autoencoder, i.e., variational autoencoder (VAE) [14, 35] , because its provided reconstruction probability density can be easily integrated into our probabilistic framework. However, outlier detection is a non-trivial task for VAE trained on multi-category noisy data. To tackle this issue, we propose a framework named Webly Supervised learning with Category-level Information (WSCI), as illustrated in Figure 1 . It can be seen from Figure 1 that our network consists of a classification network in the top flow and variational autoencder (VAE) in the bottom flow, which share common modules (i.e., CNN and encoder) and jointly leverage category-level information. The classification net-7689
Niu et al. REF adopted a more accessible category-level supervision strategy.
Learning from Noisy Web Data with Category-Level Supervision
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science