  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.
