A comprehensive survey on test-time adaptation under distribution shifts

J Liang, R He, T Tan - International Journal of Computer Vision, 2024 - Springer
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 …

Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation

R Jiao, Y Zhang, L Ding, B Xue, J Zhang, R Cai… - Computers in Biology …, 2024 - Elsevier
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Revisiting weak-to-strong consistency in semi-supervised semantic segmentation

L Yang, L Qi, L Feng, W Zhang… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
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 …

Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling

B Zhang, Y Wang, W Hou, H Wu… - Advances in …, 2021 - proceedings.neurips.cc
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 …

Freematch: Self-adaptive thresholding for semi-supervised learning

Y Wang, H Chen, Q Heng, W Hou, Y Fan, Z Wu… - arxiv preprint arxiv …, 2022 - arxiv.org
Pseudo labeling and consistency regularization approaches with confidence-based
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …

Weak-to-strong generalization: Eliciting strong capabilities with weak supervision

C Burns, P Izmailov, JH Kirchner, B Baker… - arxiv preprint arxiv …, 2023 - arxiv.org
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 …

Simmatch: Semi-supervised learning with similarity matching

M Zheng, S You, L Huang, F Wang… - Proceedings of the …, 2022 - openaccess.thecvf.com
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 …

Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency

X Luo, G Wang, W Liao, J Chen, T Song, Y Chen… - Medical Image …, 2022 - Elsevier
Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …

Semi-supervised semantic segmentation with cross pseudo supervision

X Chen, Y Yuan, G Zeng… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
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 …

End-to-end semi-supervised object detection with soft teacher

M Xu, Z Zhang, H Hu, J Wang, L Wang… - Proceedings of the …, 2021 - openaccess.thecvf.com
Previous pseudo-label approaches for semi-supervised object detection typically follow a
multi-stage schema, with the first stage to train an initial detector on a few labeled data …