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 1–25 of 265 papers

TitleStatusHype
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation ModelsCode5
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation ModelCode1
Foundation Model for Wireless Technology Recognition Using IQ Timeseries—0
Is MixIT Really Unsuitable for Correlated Sources? Exploring MixIT for Unsupervised Pre-training in Music Source Separation—0
The Efficiency of Pre-training with Objective Masking in Pseudo Labeling for Semi-Supervised Text Classification—0
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning—0
Risk Assessment Framework for Code LLMs via Leveraging Internal States—0
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object SegmentationCode0
CrystalFormer-RL: Reinforcement Fine-Tuning for Materials DesignCode2
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot MethodCode0
How much do LLMs learn from negative examples?Code0
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation—0
Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models—0
Targeted Forgetting of Image Subgroups in CLIP Models—0
Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding—0
HindiLLM: Large Language Model for Hindi—0
FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation LearningCode2
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data—0
CLAP: Unsupervised 3D Representation Learning for Fusion 3D Perception via Curvature Sampling and Prototype Learning—0
Point Cloud Unsupervised Pre-training via 3D Gaussian Splatting—0
Take Package as Language: Anomaly Detection Using TransformerCode0
DepthSplat: Connecting Gaussian Splatting and DepthCode5
SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual RepresentationsCode0
Calibrating Language Models with Adaptive Temperature ScalingCode0
Range-aware Positional Encoding via High-order Pretraining: Theory and Practice—0
Show:102550
← PrevPage 1 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