In this paper we address the limitations of the DETR-based semi-supervised object detection
(SSOD) framework particularly focusing on the challenges posed by the quality of object …

Abstract Language models (LMs) trained on vast quantities of unlabelled data have greatly
advanced the field of natural language processing (NLP). In this study, we re-visit the widely …

Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled
data to improve model performance in downstream natural language processing (NLP) …

The problem of text classification has been a mainstream research branch in natural
language processing, and how to improve the effect of classification under the scarcity of …

Recent advances in weak supervision (WS) techniques allow to mitigate the enormous cost
and effort of human data annotation for supervised machine learning by automating it using …

Correctly labeled data significantly impacts the success of deep learning for image
classification and other computer vision tasks. However, the accuracy of labels annotated by …

Semi-supervised learning uses a small amount of labeled data to guide the model and a
large amount of unlabeled data to improve its performance. Most semi-supervised object …

The performance of a model trained with\textit {noisy labels} is often improved by
simply\textit {retraining} the model with its own predicted\textit {hard} labels (ie, $1 $/$0 …

Semi-supervised learning (SSL) is a machine learning approach that integrates supervised
and unsupervised learning mechanisms. This integration may be done in different ways and …

Labeling datasets is a noteworthy challenge in machine learning, both in terms of cost and
time. This research, however, leverages an efficient answer. By exploring label propagation …