Learning with Feature-Dependent Label Noise: A Progressive Approach

Understand

Label noise is frequently observed in real-world large-scale datasets.

• The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent.
• Most existing approaches to handling noisy labels fall into two categories: they either assume an ideal feature-independent noise, or remain heuristic without theoretical guarantees.
• In this paper, we propose to target a new family of feature-dependent label noise, which is much more general than commonly used i.i.d.