Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

The label transition matrix has emerged as a widely accepted method for mitigating label
noise in machine learning. In recent years, numerous studies have centered on leveraging …

Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for
decades. The primary family of SSL algorithms, known as pseudo-labeling, involves …

Learning with noisy labels is a practically challenging problem in weakly supervised
learning. In the existing literature, open-set noises are always considered to be poisonous …

Due to the expensive costs of collecting labels in multi-label classification datasets, partially
annotated multi-label classification has become an emerging field in computer vision. One …

Learning under a continuously changing data distribution with incorrect labels is a desirable
real-world problem yet challenging. Large body of continual learning (CL) methods …

Ratings of a user to most items in recommender systems are usually missing not at random
(MNAR), largely because users are free to choose which items to rate. To achieve unbiased …

Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-
preserving multi-source decentralized learning. Existing research, relying on the assumption …

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled
data, resulting in poor generalization. The memorization effect of deep networks shows that …