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 …

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

There are a huge number of features which are said to improve Convolutional Neural
Network (CNN) accuracy. Practical testing of combinations of such features on large …

Deep learning has achieved remarkable results in many computer vision tasks. Deep neural
networks typically rely on large amounts of training data to avoid overfitting. However …

Abstract Weakly Supervised Object Localization (WSOL) techniques learn the object
location only using image-level labels, without location annotations. A common limitation for …

We propose a novel data augmentation methodGridMask'in this paper. It utilizes information
removal to achieve state-of-the-art results in a variety of computer vision tasks. We analyze …

Mixup is an advanced data augmentation method for training neural network based image
classifiers, which interpolates both features and labels of a pair of images to produce …

Deep learning algorithms have exhibited impressive performance across various computer
vision tasks; however, the challenge of overfitting persists, especially when dealing with …

By using Data Augmentation (DA), we present a new method to enhance Graph
Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised …

CutMix is a popular augmentation technique commonly used for training modern
convolutional and transformer vision networks. It was originally designed to encourage …