[HTML][HTML] Deep learning and medical image analysis for COVID-19 diagnosis and prediction
The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to
health-care organizations worldwide. To combat the global crisis, the use of thoracic …
health-care organizations worldwide. To combat the global crisis, the use of thoracic …
A review of research on co‐training
Co‐training algorithm is one of the main methods of semi‐supervised learning in machine
learning, which explores the effective information in unlabeled data by multi‐learner …
learning, which explores the effective information in unlabeled data by multi‐learner …
Fixbi: Bridging domain spaces for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies were …
representations have achieved remarkable progress. However, most of the studies were …
Cross-domain adaptive clustering for semi-supervised domain adaptation
In semi-supervised domain adaptation, a few labeled samples per class in the target domain
guide features of the remaining target samples to aggregate around them. However, the …
guide features of the remaining target samples to aggregate around them. However, the …
Clda: Contrastive learning for semi-supervised domain adaptation
A Singh - Advances in Neural Information Processing …, 2021 - proceedings.neurips.cc
Abstract Unsupervised Domain Adaptation (UDA) aims to align the labeled source
distribution with the unlabeled target distribution to obtain domain invariant predictive …
distribution with the unlabeled target distribution to obtain domain invariant predictive …
Conflict-based cross-view consistency for semi-supervised semantic segmentation
Semi-supervised semantic segmentation (SSS) has recently gained increasing research
interest as it can reduce the requirement for large-scale fully-annotated training data. The …
interest as it can reduce the requirement for large-scale fully-annotated training data. The …
Semi-supervised domain adaptation with source label adaptation
Abstract Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen
target data with a few labeled and lots of unlabeled target data, along with many labeled …
target data with a few labeled and lots of unlabeled target data, along with many labeled …
Semi-supervised domain adaptation based on dual-level domain mixing for semantic segmentation
Data-driven based approaches, in spite of great success in many tasks, have poor
generalization when applied to unseen image domains, and require expensive cost of …
generalization when applied to unseen image domains, and require expensive cost of …
Semi-supervised vision transformers
We study the training of Vision Transformers for semi-supervised image classification.
Transformers have recently demonstrated impressive performance on a multitude of …
Transformers have recently demonstrated impressive performance on a multitude of …
Learning semantic correspondence with sparse annotations
Finding dense semantic correspondence is a fundamental problem in computer vision,
which remains challenging in complex scenes due to background clutter, extreme intra-class …
which remains challenging in complex scenes due to background clutter, extreme intra-class …