Details

Autor(en) / Beteiligte

• Yuncheng Li
• Jianchao Yang
• Yale Song
• Liangliang Cao
• Jiebo Luo
• Li-Jia Li

Titel

Learning from Noisy Labels with Distillation

Ist Teil von

• 2017 IEEE International Conference on Computer Vision (ICCV), 2017, p.1928-1936

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise has been treated as statistical outliers, and techniques such as importance re-weighting and bootstrapping have been proposed to alleviate the problem. According to our observation, the real-world noisy labels exhibit multimode characteristics as the true labels, rather than behaving like independent random outliers. In this work, we propose a unified distillation framework to use "side" information, including a small clean dataset and label relations in knowledge graph, to "hedge the risk" of learning from noisy labels. Unlike the traditional approaches evaluated based on simulated label noises, we propose a suite of new benchmark datasets, in Sports, Species and Artifacts domains, to evaluate the task of learning from noisy labels in the practical setting. The empirical study demonstrates the effectiveness of our proposed method in all the domains.