Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …

Learning from corrupted data marginally degrades model performance. As deep learning
proliferates, the need for large, accurately labeled datasets becomes crucial. Central to this …

In general, deep neural network is vulnerable to noisy labels also known as erroneous
labels. As a main solution to mitigate this problem, sample selection techniques have been …

We propose a framework using contrastive learning as a pre-training task to perform image
classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling …

This paper proposes a novel and efficient method for Learning from Label Proportions (LLP),
whose goal is to train a classifier only by using the class label proportions of instance sets …