Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …

Test-time adaptation provides an effective means of tackling the potential distribution shift
between model training and inference, by dynamically updating the model at test time. This …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

Real-world data often exhibits long tail distributions with heavy class imbalance, where the
majority classes can dominate the training process and alter the decision boundaries of the …

The networks trained on the long-tailed dataset vary remarkably, despite the same training
settings, which shows the great uncertainty in long-tailed learning. To alleviate the …

Abstract We introduce Retrieval Augmented Classification (RAC), a generic approach to
augmenting standard image classification pipelines with an explicit retrieval module. RAC …

Online updating of time series forecasting models aims to address the concept drifting
problem by efficiently updating forecasting models based on streaming data. Many …

Many recent machine learning tasks focus to develop models that can generalize to unseen
distributions. Domain generalization (DG) has become one of the key topics in various fields …

To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …