Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …

Driven by improved architectures and better representation learning frameworks, the field of
visual recognition has enjoyed rapid modernization and performance boost in the early …

This paper does not describe a novel method. Instead, it studies a straightforward,
incremental, yet must-know baseline given the recent progress in computer vision: self …

The success of language Transformers is primarily attributed to the pretext task of masked
language modeling (MLM), where texts are first tokenized into semantically meaningful …

Visual recognition in low-data regimes requires deep neural networks to learn generalized
representations from limited training samples. Recently, CLIP-based methods have shown …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Diffusion probabilistic models (DPMs) have achieved remarkable quality in image
generation that rivals GANs'. But unlike GANs, DPMs use a set of latent variables that lack …

Learning visual representations of medical images (eg, X-rays) is core to medical image
understanding but its progress has been held back by the scarcity of human annotations …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …