Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

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 …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Pseudo labeling and consistency regularization approaches with confidence-based
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …

Attention-based neural networks such as the Vision Transformer (ViT) have recently attained
state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient …

Learning with few labeled data has been a longstanding problem in the computer vision and
machine learning research community. In this paper, we introduced a new semi-supervised …

Convolutional architectures have proven extremely successful for vision tasks. Their hard
inductive biases enable sample-efficient learning, but come at the cost of a potentially lower …

Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large
computer vision benchmarks. A successful approach to SSL is to learn embeddings which …

Humans' ability to transfer knowledge through teaching is one of the essential aspects for
human intelligence. A human teacher can track the knowledge of students to customize the …

Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage
unlabeled data. In this work, we first construct a strong baseline of self-training (namely ST) …