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 …

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 …

Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

Unsupervised visual representation learning has gained much attention from the computer
vision community because of the recent achievement of contrastive learning. Most of the …

We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision
Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate …

This paper presents solo-learn, a library of self-supervised methods for visual representation
learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both …

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an
encoder which can be used as a general-purpose feature extractor, such that downstream …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

Vision transformers (ViTs) are usually considered to be less light-weight than convolutional
neural networks (CNNs) due to the lack of inductive bias. Recent works thus resort to …

Vision transformers (ViTs) inherited the success of NLP but their structures have not been
sufficiently investigated and optimized for visual tasks. One of the simplest solutions is to …