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 81–90 of 265 papers

TitleStatusHype
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
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
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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