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

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Mixup is a popular data augmentation technique based on taking convex combinations of
pairs of examples and their labels. This simple technique has been shown to substantially …

The existing deep networks have shown excellent performance in remote sensing scene
classification (RSSC), which generally requires a large amount of class-balanced training …

Data mixing augmentation have proved to be effective for improving the generalization
ability of deep neural networks. While early methods mix samples by hand-crafted policies …

We propose the first unified theoretical analysis of mixed sample data augmentation
(MSDA), such as Mixup and CutMix. Our theoretical results show that regardless of the …

Recent strategies achieved ensembling"" for free"" by fitting concurrently diverse
subnetworks inside a single base network. The main idea during training is that each …

Abstract Whole Slide Images (WSIs) are usually gigapixel in size and lack pixel-level
annotations. The WSI datasets are also imbalanced in categories. These unique …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …