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CAVA: Complementary-Label-Aware Data Valuation

Tan-Ha Mai, Hsuan-Tien Lin, Vu Nguyen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26734 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Os dados semânticos de transferência de valores com base em valores de parâmetros (CLL) são baratos para serem coletados a partir de multidões ou modelos de linguagem de visão (OTMs), tornando-os atraentes para anotações de grande escala. No entanto, ambas as fontes podem atribuir erradamente a classe verdadeira como o rótulo complementar. Esses erros de anotação são particularmente prejudiciais no CLL porque o modelo é explicitamente treinado para suprimir a resposta correta. Os dados de avaliação de transporte óptimo (OT) identificam amostras prejudiciais, comparando os dados de treinamento com um conjunto de validação limpa, mas transferindo-os diretamente para o CLL cria um equilíbrio semântico: o rótulo $c$ não significa $c$ na formação, mas

Machine translation (Meta M2M100); the English original follows.

Abstract

Complementary-label learning (CLL) trains a classifier from labels that name a class an instance does not belong to. Such labels are cheap to collect from crowdworkers or vision-language models (VLMs), making them attractive for large-scale annotation. However, both sources can mistakenly assign the true class as the complementary label. Such annotation errors are particularly harmful in CLL because the model is explicitly trained to suppress the correct answer. Optimal transport (OT) data valuation identifies harmful samples by comparing training data with a clean validation set, but transferring it directly to CLL creates a semantic mismatch: label $c$ means not class $c$ in the training but class $c$ in the validation. We show that this makes the OT label groups noisy mixtures whose behavior depends on the noise rate. The transferred score is informative only at sufficiently high noise, becomes uninformative at the crossover rate $1/M$ for $M$ classes, and reverses below it, where practical CLL annotation typically operates. We propose CAVA, which introduces a complementary label cost and a label margin that isolates the contribution of each complementary label to the transport potential. The margin keeps a consistent direction across noise rates, detects diagonal noise with a parameter-free threshold of closed-form precision, and allows detected samples to be recycled as ordinary-label supervision. On synthetic and human-annotated benchmarks, CAVA separates diagonal noise almost perfectly where directly transferred OT valuation performs worse than random. With only a fraction of the clean validation data, it also improves every CLL objective we evaluate, showing that OT-based valuation becomes reliable once the semantics of complementary supervision are modeled explicitly.

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Topics: Computer science, Classifier (UML), Valuation (finance), Diagonal, Artificial intelligence, Data mining

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