As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Continual learning is a realistic learning scenario for AI models. Prevalent scenario of
continual learning, however, assumes disjoint sets of classes as tasks and is less realistic …

Abstract Many studies on Graph Data Augmentation (GDA) approaches have emerged. The
techniques have rapidly improved performance for various graph neural network (GNN) …

Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …

Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but
efficient synthetic training datasets from large ones. Existing DD methods based on gradient …

Data augmentation is a widely used trick when training deep neural networks: in addition to
the original data, properly transformed data are also added to the training set. However, to …

Generative data augmentation, which scales datasets by obtaining fake labeled examples
from a trained conditional generative model, boosts classification performance in various …

Data augmentation is a cornerstone of the machine learning pipeline, yet its theoretical
underpinnings remain unclear. Is it merely a way to artificially augment the data set size? Or …