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In this paper, we investigate the problem of learning with noisy labels inreal-world annotation scenarios, where noise can be categorized into two types:factual noise and ambiguity noise. To better distinguish these noise types andutilize their semantics, we propose a novel sample selection-based approach fornoisy label learning, called Proto-semi. Proto-semi initially divides allsamples into the confident and unconfident datasets via warm-up. By leveragingthe confident dataset, prototype vectors are constructed to capture classcharacteristics. Subsequently, the distances between the unconfident samplesand the prototype vectors are calculated to facilitate noise classification.Based on these distances, the labels are either corrected or retained,resulting in the refinement of the confident and unconfident datasets. Finally,we introduce a semi-supervised learning method to enhance training. Empiricalevaluations on a real-world annotated dataset substantiate the robustness ofProto-semi in handling the problem of learning from noisy labels. Meanwhile,the prototype-based repartitioning strategy is shown to be effective inmitigating the adverse impact of label noise. Our code and data are availableat https://github.com/fuxiAIlab/ProtoSemi.

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective