Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness

TL;DR

This paper introduces a new label noise model considering multiple labelers, including malicious ones, and proposes a robust framework to filter noisy labels, improving learning accuracy in crowdsourced annotation scenarios.

Contribution

It generalizes instance-dependent noise models to multiple labelers and develops a labeler-aware framework that withstands adversarial label attacks.

Findings

Adversarial label attacks defeat current state-of-the-art methods.

The proposed framework effectively filters noisy labels in adversarial settings.

Model remains robust even with extreme adversarial label noise.

Abstract

Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as class-conditional transition matrices. More recent work on instance-dependent noise models are more realistic, but assume a single generative process for label noise across the entire dataset. We propose a more principled model of label noise that generalizes instance-dependent noise to multiple labelers, based on the observation that modern datasets are typically annotated using distributed crowdsourcing methods. Under our labeler-dependent model, label noise manifests itself under two modalities: natural error of good-faith labelers, and adversarial labels provided by malicious actors. We present two adversarial attack vectors that more accurately reflect the label noise that may be encountered in real-world settings, and demonstrate that under our multimodal noisy labels model, state-of-the-art approaches for learning from noisy labels are defeated by adversarial label attacks. Finally, we propose a multi-stage, labeler-aware, model-agnostic framework that reliably filters noisy labels by leveraging knowledge about which data partitions were labeled by which labeler, and show that our proposed framework remains robust even in the presence of extreme adversarial label noise.