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 …
Boosting semi-supervised learning by exploiting all unlabeled data
Semi-supervised learning (SSL) has attracted enormous attention due to its vast potential of
mitigating the dependence on large labeled datasets. The latest methods (eg, FixMatch) use …
mitigating the dependence on large labeled datasets. The latest methods (eg, FixMatch) use …
Revisiting consistency regularization for semi-supervised learning
Consistency regularization is one of the most widely-used techniques for semi-supervised
learning (SSL). Generally, the aim is to train a model that is invariant to various data …
learning (SSL). Generally, the aim is to train a model that is invariant to various data …
Dc-ssl: Addressing mismatched class distribution in semi-supervised learning
Consistency-based Semi-supervised learning (SSL) has achieved promising performance
recently. However, the success largely depends on the assumption that the labeled and …
recently. However, the success largely depends on the assumption that the labeled and …
Pefat: Boosting semi-supervised medical image classification via pseudo-loss estimation and feature adversarial training
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 …
(SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding …
Semi-supervised object detection via multi-instance alignment with global class prototypes
Semi-Supervised object detection (SSOD) aims to improve the generalization ability of
object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD …
object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD …
Laplacenet: A hybrid graph-energy neural network for deep semisupervised classification
P Sellars, AI Aviles-Rivero… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Semisupervised learning (SSL) has received a lot of recent attention as it alleviates the need
for large amounts of labeled data which can often be expensive, requires expert knowledge …
for large amounts of labeled data which can often be expensive, requires expert knowledge …
Computational Evaluation of the Combination of Semi-Supervised and Active Learning for Histopathology Image Segmentation with Missing Annotations
Real-world segmentation tasks in digital pathology require a great effort from human experts
to accurately annotate a sufficiently high number of images. Hence, there is a huge interest …
to accurately annotate a sufficiently high number of images. Hence, there is a huge interest …
Fishermatch: Semi-supervised rotation regression via entropy-based filtering
Estimating the 3DoF rotation from a single RGB image is an important yet challenging
problem. Recent works achieve good performance relying on a large amount of expensive …
problem. Recent works achieve good performance relying on a large amount of expensive …
Towards semi-supervised learning with non-random missing labels
Semi-supervised learning (SSL) tackles the label missing problem by enabling the effective
usage of unlabeled data. While existing SSL methods focus on the traditional setting, a …
usage of unlabeled data. While existing SSL methods focus on the traditional setting, a …