Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

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 …

Large language models (LLMs) have been recently leveraged as training data generators
for various natural language processing (NLP) tasks. While previous research has explored …

In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch
from semi-supervised classification, where the prediction of a weakly perturbed image …

The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised
learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a …

Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Widely used alignment techniques, such as reinforcement learning from human feedback
(RLHF), rely on the ability of humans to supervise model behavior-for example, to evaluate …

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 …

In this paper, we study the semi-supervised semantic segmentation problem via exploring
both labeled data and extra unlabeled data. We propose a novel consistency regularization …