Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

The need for organ transplants has risen, but the number of available organ donations for
transplants has stagnated worldwide. Regenerative medicine has been developed to make …

The recent research in semi-supervised learning (SSL) is mostly dominated by consistency
regularization based methods which achieve strong performance. However, they heavily …

Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet
pre-training is commonly used to initialize the backbones of object detection and …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet,
which is 2.0% better than the state-of-the-art model that requires 3.5 B weakly labeled …

Recent advances in domain adaptation show that deep self-training presents a powerful
means for unsupervised domain adaptation. These methods often involve an iterative …

Semi-supervised learning has been an effective paradigm for leveraging unlabeled data to
reduce the reliance on labeled data. We propose CoMatch, a new semi-supervised learning …

Semi-supervised learning, ie jointly learning from labeled and unlabeled samples, is an
active research topic due to its key role on relaxing human supervision. In the context of …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …