Trustable Co-label Learning from Multiple Noisy Annotators

TL;DR

This paper introduces Trustable Co-label Learning (TCL), a data-efficient method that leverages a small trusted dataset to improve deep classifier robustness from multiple noisy annotators by jointly learning classifiers and label aggregation.

Contribution

It proposes a novel coupled-view learning approach that uses trusted data to generate reliable soft labels, enhancing learning from noisy annotations, with improvements for complete data cases.

Findings

TCL outperforms existing methods on synthetic and real datasets.

The approach effectively utilizes trusted data to improve robustness.

Enhanced model capacity with multilayer neural networks for complete data cases.

Abstract

Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multiple noisy annotators. Numerous earlier works assume that all labels are noisy, while it is usually the case that a few trusted samples with clean labels are available. This raises the following important question: how can we effectively use a small amount of trusted data to facilitate robust classifier learning from multiple annotators? This paper proposes a data-efficient approach, called \emph{Trustable Co-label Learning} (TCL), to learn deep classifiers from multiple noisy annotators when a small set of trusted data is available. This approach follows the coupled-view learning manner, which jointly learns the data classifier and the label aggregator. It effectively uses trusted data as a guide to generate trustable soft labels (termed co-labels). A co-label learning can then be performed by alternately reannotating the pseudo labels and refining the classifiers. In addition, we further improve TCL for a special complete data case, where each instance is labeled by all annotators and the label aggregator is represented by multilayer neural networks to enhance model capacity. Extensive experiments on synthetic and real datasets clearly demonstrate the effectiveness and robustness of the proposed approach. Source code is available at https://github.com/ShikunLi/TCL