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The label noise transition matrix, characterizing the probabilities of atraining instance being wrongly annotated, is crucial to designing popularsolutions to learning with noisy labels. Existing works heavily rely on finding"anchor points" or their approximates, defined as instances belonging to aparticular class almost surely. Nonetheless, finding anchor points remains anon-trivial task, and the estimation accuracy is also often throttled by thenumber of available anchor points. In this paper, we propose an alternativeoption to the above task. Our main contribution is the discovery of anefficient estimation procedure based on a clusterability condition. We provethat with clusterable representations of features, using up to third-orderconsensuses of noisy labels among neighbor representations is sufficient toestimate a unique transition matrix. Compared with methods using anchor points,our approach uses substantially more instances and benefits from a much bettersample complexity. We demonstrate the estimation accuracy and advantages of ourestimates using both synthetic noisy labels (on CIFAR-10/100) and realhuman-level noisy labels (on Clothing1M and our self-collected human-annotatedCIFAR-10). Our code and human-level noisy CIFAR-10 labels are available athttps://github.com/UCSC-REAL/HOC.

Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels