Learning from Noisy Web Data with Category-level Supervision

TL;DR

This paper introduces a novel webly supervised learning approach that uses category-level supervision and variational autoencoders to effectively handle noisy web data, improving learning performance without heavy manual labeling.

Contribution

Proposes a VAE-based method leveraging category-level supervision to address label noise in web data, reducing reliance on manual annotation and enhancing learning from web resources.

Findings

Effective in reducing label noise impact

Improves performance on benchmark datasets

Leverages category-level semantic information

Abstract

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. Nevertheless, the performance gap between webly supervised learning and traditional supervised learning is still very large, owning to the label noise of web data. To be exact, the labels of images crawled from public websites are very noisy and often inaccurate. Some existing works tend to facilitate learning from web data with the aid of extra information, such as augmenting or purifying web data by virtue of instance-level supervision, which is usually in demand of heavy manual annotation. Instead, we propose to tackle the label noise by leveraging more accessible category-level supervision. In particular, we build our method upon variational autoencoder (VAE), in which the classification network is attached on the hidden layer of VAE in a way that the classification network and VAE can jointly leverage the category-level hybrid semantic information. The effectiveness of our proposed method is clearly demonstrated by extensive experiments on three benchmark datasets.