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 151–175 of 265 papers

TitleStatusHype
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training—0
On Architectures and Training for Raw Waveform Feature Extraction in ASR—0
Foundation Model for Wireless Technology Recognition Using IQ Timeseries—0
GFlowNet Pretraining with Inexpensive Rewards—0
GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method—0
HindiLLM: Large Language Model for Hindi—0
Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment—0
Improving On-Screen Sound Separation for Open-Domain Videos with Audio-Visual Self-Attention—0
Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Is MixIT Really Unsuitable for Correlated Sources? Exploring MixIT for Unsupervised Pre-training in Music Source Separation—0
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback—0
Large Language Model Enabled Semantic Communication Systems—0
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning—0
Learning Discriminative Features with Class Encoder—0
Learning Non-Linear Reconstruction Models for Image Set Classification—0
Learning Unsupervised Gaze Representation via Eye Mask Driven Information Bottleneck—0
Leveraging Random Label Memorization for Unsupervised Pre-Training—0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
Machine Translation Pre-training for Data-to-Text Generation - A Case Study in Czech—0
Research on CPI Prediction Based on Natural Language Processing—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Risk Assessment Framework for Code LLMs via Leveraging Internal States—0
R-LAtte: Attention Module for Visual Control via Reinforcement Learning—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Show:102550
← PrevPage 7 of 11Next →
All datasetsMeaslesUCI measles

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