Although deep learning has achieved satisfactory performance in computer vision, a large
volume of images is required. However, collecting images is often expensive and …

Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Data augmentation is one of the most prevalent tools in deep learning, underpinning many
recent advances, including those from classification, generative models, and representation …

Recent studies in deepfake detection have yielded promising results when the training and
testing face forgeries are from the same dataset. However, the problem remains challenging …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Traffic sign detection is a challenging task for the unmanned driving system, especially for
the detection of multi-scale targets and the real-time problem of detection. In the traffic sign …

Recent work on automated augmentation strategies has led to state-of-the-art results in
image classification and object detection. An obstacle to a large-scale adoption of these …

Much research on object detection focuses on building better model architectures and
detection algorithms. Changing the model architecture, however, comes at the cost of …

Data augmentation is an essential technique for improving generalization ability of deep
learning models. Recently, AutoAugment\cite {cubuk2018autoaugment} has been proposed …

Data augmentation is a commonly used technique for increasing both the size and the
diversity of labeled training sets by leveraging input transformations that preserve …