Machine learning methods strive to acquire a robust model during training that can
generalize well to test samples, even under distribution shifts. However, these methods often …

Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

Semi-supervised learning (SSL) is a promising approach to reduce the labeling burden in
remote sensing scene classification tasks. However, most semi-supervised methods …

Failure to recognize samples from the classes unseen during training is a major limitation of
artificial intelligence in the real-world implementation for recognition and classification of …

Abstract Open-world Semi-Supervised Learning (OSSL) is a realistic and challenging task,
aiming to classify unlabeled samples from both seen and novel classes using partially …

Recent advances in robust semi-supervised learning (SSL) typical filters out-of-distribution
(OOD) information at the sample level. We argue that an overlooked problem of robust SSL …

Pseudo-labeling approaches have been proven beneficial for semi-supervised learning
(SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding …

In typical medical image classification problems labeled data is scarce while unlabeled data
is more available. Semi-supervised learning and self-supervised learning are two different …