The presence of label noise in the training data has a profound impact on the generalization
of deep neural networks (DNNs). In this study, we introduce and theoretically demonstrate a …

Supervised deep learning has achieved tremendous success in speaker verification.
However, deep speaker models tend to overfit noisy labels when they are present in the …

Graph anomaly detection has long been an important problem in various domains
pertaining to information security such as financial fraud, social spam, network intrusion, etc …

Large datasets with noisy labels and high dimensions have become increasingly prevalent
in industry. These datasets often contain errors or inconsistencies in the assigned labels and …

Deep-learning methods rely on massive labeled data, which has become one of the main
impediments in hyperspectral image change detection (HSI-CD). To resolve this problem …

Noise label exists widely in real-world data, resulting in the degradation of classification
performance. Popular methods require a known noise distribution or additional cleaning …

Recently, intensive attempts have been made to design robust models for fine-grained
visual recognition, most notably are the impressive gains for training with noisy labels by …

Deep neural networks (DNNs) are often prone to learn the spurious correlations between
target classes and bias attributes, like gender and race, inherent in a major portion of …

Remote sensing (RS) scene classification has always attracted much attention as an
elemental and hot topic in the RS community. In recent years, many methods using …

Most recent progress in natural language understanding (NLU) has been driven, in part, by
benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU models have now …