A three-stage self-training framework for semi-supervised semantic segmentation
Semantic segmentation has been widely investigated in the community, in which state-of-the-
art techniques are based on supervised models. Those models have reported …
art techniques are based on supervised models. Those models have reported …
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 …
Semi-supervised medical image classification based on attention and intrinsic features of samples
Z Zhou, C Lu, W Wang, W Dang, K Gong - Applied Sciences, 2022 - mdpi.com
The training of deep neural networks usually requires a lot of high-quality data with good
annotations to obtain good performance. However, in clinical medicine, obtaining high …
annotations to obtain good performance. However, in clinical medicine, obtaining high …
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive
amounts of unlabeled data. To better exploit the unlabeled data the latest SSL methods use …
amounts of unlabeled data. To better exploit the unlabeled data the latest SSL methods use …