Stochastic Order Learning associates each instance with multiple plausible ranks and trains embeddings via complementary discriminative and stochastic-order losses that remain robust to ordinal label noise.
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CARE is a parameter-efficient framework that aggregates predictions from noisy labels, VLM text embeddings, and visual features with class-frequency-based agreement thresholds to rectify labels in long-tailed noisy datasets.
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Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
Stochastic Order Learning associates each instance with multiple plausible ranks and trains embeddings via complementary discriminative and stochastic-order losses that remain robust to ordinal label noise.
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CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels
CARE is a parameter-efficient framework that aggregates predictions from noisy labels, VLM text embeddings, and visual features with class-frequency-based agreement thresholds to rectify labels in long-tailed noisy datasets.