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SESQA: Semi-supervised learning for speech quality assessment
SESQA: a semi-supervised learning approach for speech quality assessment -
Organ-MNIST
A dataset for active semi-supervised learning for class imbalanced datasets -
Path-MNIST
A dataset for active semi-supervised learning for class imbalanced datasets -
Graph-based Active Learning
The dataset used in the paper is a graph-based active learning problem, where the goal is to achieve a low error rate while querying as few nodes as possible. -
Spleen dataset
The dataset used for training and testing the proposed deep co-training method for semi-supervised image segmentation. -
Atrial Segmentation Challenge dataset
Semantic object segmentation is a fundamental task in medical image analysis and has been widely used in automatic delineation of regions of interest in 3D medical images, such... -
SCGM dataset
The dataset used for training and testing the proposed deep co-training method for semi-supervised image segmentation. -
NIH pancreas segmentation dataset
The NIH pancreas segmentation dataset contains 82 abdominal CT volumes. The width and height of each volume are 512, while the axial view slice number can vary from 181 to 466.... -
Word representations
Word representations: A simple and general method for semi-supervised learning. -
Graph Fairing Convolutional Networks for Anomaly Detection
Graph Fairing Convolutional Networks for Anomaly Detection -
FedNST: Federated Noisy Student Training for Automatic Speech Recognition
Federated Noisy Student Training for Automatic Speech Recognition -
GraphXCOVID
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays -
VOC-Mixture
The dataset used in the paper for semi-supervised object detection tasks. -
COCO-Standard
The dataset used in the paper for semi-supervised object detection tasks.
