REsponsible & Accountable Learning (REAL)
@ University of California, Santa Cruz

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To download our collected noisy labels, click the link below:

 Download now

import torch
# For CIFAR-10N noisy labels
noise_label = torch.load('CIFAR-10_human.pt') 
clean_label = noise_label['clean_label'] 
worst_label = noise_label['worse_label'] 
aggre_label = noise_label['aggre_label'] 
random_label1 = noise_label['random_label1'] 
random_label2 = noise_label['random_label2'] 
random_label3 = noise_label['random_label3']
# For CIFAR-100N noisy labels
noise_label = torch.load('CIFAR-100_human.pt') 
clean_label = noise_label['clean_label'] 
noisy_label = noise_label['noisy_label'] 

For more details, i.e., the starter code and tensorflow dataloader, please see this Github, or click the above Github icon.