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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. -
Doubly Robust Self-Training
Self-training is an important technique for solving semi-supervised learning problems. It leverages unlabeled data by generating pseudo-labels and combining them with a limited... -
IDRC 2002 Shootout
The shootout dataset is a regression dataset containing information about the reflectance spectra of pharmaceutical tablets. -
InstanT: Semi-supervised Learning with Instance-dependent Thresholds
Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves... -
Semi-supervised sequence classification through change point detection
Sequential sensor data is generated in a wide variety of practical applications. A fundamental challenge involves learning effective classifiers for such sequential data. While... -
Heart2Heart
A new semi-supervised learning benchmark for classifying view and diagnosing aortic stenosis from the 6th Ma- -
Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data
Semi-supervised learning from uncurated unlabelled data -
Self-supervised and semi-supervised learning for GANs
Self-supervised and semi-supervised learning for GANs -
Semi-supervised conditional GANs
Semi-supervised conditional GANs -
QS-TTS: A Semi-Supervised Text-to-Speech Framework
QS-TTS is a semi-supervised TTS framework based on VQ-S3RL to effectively utilize more unlabeled speech audio to improve TTS quality while reducing its requirements for... -
End-to-End Semi-Supervised Object Detection with Soft Teacher
The proposed end-to-end pseudo-label based semi-supervised object detection framework, which simultaneously performs pseudo-labeling for unlabelled images and trains a detector... -
Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recogn...
EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often... -
Open-Set Semi-Supervised Object Detection
Open-Set Semi-Supervised Object Detection aims to leverage the unconstrained unlabeled images to improve an object detector trained with the available labeled data. -
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
