arXiv:2606.14965v1 Announce Type: new Abstract: Synthetic instance-dependent label noise (IDN) benchmarks are widely used to evaluate noisy-label learning methods, yet existing approaches typically generate noise through imperfect annotators or classifier raters, leaving the source of ambiguity implicit. We introduce CILN, a benchmark generation framework that creates IDN through controlled input corruptions. A diverse voter pool labels corrupted instances, producing benchmark datasets in which both the source and severity of ambiguity are explicit and controllable. Using CIFAR10, MNIST, and A
The proliferation of AI models demands increasingly robust and reliable training data, making the explicit understanding and control of label noise critical for improving model performance and generalization.
A strategic reader should care because better handling of instance-dependent label noise can lead to more accurate, robust, and deployable AI systems, reducing development costs and improving real-world application reliability.
This new benchmark generation framework explicitly defines and controls the source and severity of ambiguity in synthetic label noise, allowing for more systematic evaluation and development of noisy-label learning methods.
- · AI researchers
- · Machine learning developers
- · Data scientists
- · Deep learning platforms
- · AI models sensitive to noisy data
- · Organizations relying on low-quality datasets
Improved benchmark datasets for instance-dependent label noise research.
Development of more effective and reliable algorithms for learning with noisy labels.
Enhanced trust and broader adoption of AI systems in real-world applications due to improved robustness.
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Read at arXiv cs.LG