noisy-learning

Introduces CILN, a framework for generating instance-dependent label noise benchmarks through controlled input corruptions, enabling explicit control over ambiguity source and severity. Experiments show it produces realistic noise structures and exposes failure modes in popular noisy-label learning methods.