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

Understand

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.