Deep　neural　networks　typically　require　a　large　number　of　accurately　labeled　images　for　training　with　cross-entropy　loss,　and　often　overfit　noisy　labels.　Contrastive　learning　has　proven　impressive　in　noisy　label　learning　because　it　can　learn　discrimination　representations.　However,　the　weak　correlation　between　the　samples　and　their　semantic　class,　which　ignores　the　correlation　between　the　instances　and　labels,　as　well　as　the　instance　semantic　divergence　with　the　same　label,　may　inevitably　lead　to　class　collisions,　hampering　the　label　correction.　To　address　these　problems,　this　study　proposes　a　noisy　label　learning　framework　that　performs　label　correction　and　constructs　a　contrastive　prototypical　classifier　cooperatively.　In　particular,　the　prototypical　classifier　maximizes　the　distance　between　the　instances　and　class　prototypes　to　improve　the　intraclass　compactness　using　contrastive　prototypical　loss.　Furthermore,　we　provide　a　theoretical　guarantee　that　the　contrastive　prototypical　loss　has　a　smaller　Lipschitz　constant　and　boosts　the　robustness.　Motivated　by　the　theoretical　analysis,　this　framework　performs　label　correction　using　the　prediction　of　a　contrastive　prototypical　classifier.　Extensive　experiments　demonstrate　that　the　proposed　framework　achieves　superior　classification　accuracy　on　synthetic　datasets　with　various　noise　patterns　and　levels.　©　2023　Elsevier　Inc.