SOTAVerified

Unsupervised Pre-training

Pre-training a neural network using unsupervised (self-supervised) auxiliary tasks on unlabeled data.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 265 papers

TitleStatusHype
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data—0
Unsupervised pre-training for sequence to sequence speech recognition—0
Unsupervised Pre-Training for 3D Leaf Instance Segmentation—0
Unsupervised Pre-training for Biomedical Question Answering—0
Unsupervised Pre-training for Natural Language Generation: A Literature Review—0
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors—0
Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project—0
Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project—0
Unsupervised pre-training helps to conserve views from input distribution—0
Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis—0
Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models—0
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference—0
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain—0
Weakly Supervised Construction of ASR Systems with Massive Video Data—0
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?—0
What Makes for Good Views for Contrastive Learning?—0
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification—0
3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents—0
A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology—0
Adversarial Ladder Networks—0
An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
115RDLsAccuracy (%)95—Unverified
29RDLsAccuracy (%)94—Unverified
33 RMDLAccuracy (%)93—Unverified
4CNNAccuracy (%)73—Unverified
5RMDLAccuracy (%)0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1RMDL (30 RDLs)Sensitivity (VEB)90.69—Unverified
2Sensitivity89.1—Unverified
3RMDL 3 RDLsSensitivity0.87—Unverified