A comprehensive survey on test-time adaptation under distribution shifts
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 …
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
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
clinical approaches. Recent success of deep learning-based segmentation methods usually …
Revisiting weak-to-strong consistency in semi-supervised semantic segmentation
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 …
from semi-supervised classification, where the prediction of a weakly perturbed image …
Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling
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 …
learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a …
Freematch: Self-adaptive thresholding for semi-supervised learning
Pseudo labeling and consistency regularization approaches with confidence-based
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …
Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
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 …
(RLHF), rely on the ability of humans to supervise model behavior-for example, to evaluate …
Simmatch: Semi-supervised learning with similarity matching
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 …
machine learning research community. In this paper, we introduced a new semi-supervised …
Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency
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 …
performance in many medical image segmentation tasks, they rely on a large set of labeled …
Semi-supervised semantic segmentation with cross pseudo supervision
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 …
both labeled data and extra unlabeled data. We propose a novel consistency regularization …
End-to-end semi-supervised object detection with soft teacher
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 …
multi-stage schema, with the first stage to train an initial detector on a few labeled data …