Iterative Learning with Open-set Noisy Labels

Understand

Large-scale datasets possessing clean label annotations are crucial for training Convolutional Neural Networks (CNNs).

• However, labeling large-scale data can be very costly and error-prone, and even high-quality datasets are likely to contain noisy (incorrect) labels.
• Existing works usually employ a closed-set assumption, whereby the samples associated with noisy labels possess a true class contained within the set of known classes in the training data.
• However, such an assumption is too restrictive for many applications, since samples associated with noisy labels might in fact possess a true class that is not present in the training data.