Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Abstract Natural Language Processing (NLP) is one of the most captivating applications of
Deep Learning. In this survey, we consider how the Data Augmentation training strategy can …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

Contrastive learning has been widely applied to graph representation learning, where the
view generators play a vital role in generating effective contrastive samples. Most of the …

Invariances to translations have imbued convolutional neural networks with powerful
generalization properties. However, we often do not know a priori what invariances are …

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node
classification, regression, and recommendation tasks. GNNs work well when rich and high …

Optimization of image transformation functions for the purpose of data augmentation has
been intensively studied. In particular, adversarial data augmentation strategies, which …