PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels

Benchmark Model Rank Results
image-classification-on-mini-webvision-1-0PSSCL (130 epochs)#10Top-1 Accuracy: 79.56Top-5 Accuracy: 94.84
image-classification-on-mini-webvision-1-0PSSCL (120 epochs)#19Top-1 Accuracy: 78.52Top-5 Accuracy: 93.80
learning-with-noisy-labels-on-animalPSSCL#4Accuracy: 88.74Network: Vgg19-BNImageNet Pretrained: NO
learning-with-noisy-labels-on-cifar-100nPSSCL#3Accuracy (mean): 72.00
learning-with-noisy-labels-on-cifar-10nPSSCL#2Accuracy (mean): 96.41
learning-with-noisy-labels-on-cifar-10n-1PSSCL#2Accuracy (mean): 96.17
learning-with-noisy-labels-on-cifar-10n-2PSSCL#1Accuracy (mean): 96.21
learning-with-noisy-labels-on-cifar-10n-3PSSCL#1Accuracy (mean): 96.49
learning-with-noisy-labels-on-cifar-10n-worstPSSCL#2Accuracy (mean): 95.12
learning-with-noisy-labels-on-food-101PSSCL#2Accuracy (% ): 86.41