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 226–250 of 265 papers

TitleStatusHype
Color Constancy Convolutional Autoencoder—0
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection—0
Unsupervised pre-training helps to conserve views from input distribution—0
Unsupervised Pre-Training of Image Features on Non-Curated DataCode0
wav2vec: Unsupervised Pre-training for Speech RecognitionCode1
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task LearningCode0
Tuning Multilingual Transformers for Named Entity Recognition on Slavic LanguagesCode0
Multilingual Constituency Parsing with Self-Attention and Pre-TrainingCode1
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent AgentsCode0
Leveraging Random Label Memorization for Unsupervised Pre-Training—0
Advancing PICO Element Detection in Biomedical Text via Deep Neural NetworksCode0
Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation—0
Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power—0
Deep Discriminative Model for Video Classification—0
RMDL: Random Multimodel Deep Learning for ClassificationCode0
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks—0
Post-training for Deep Learning—0
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
A Pitfall of Unsupervised Pre-Training—0
Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning—0
A Pitfall of Unsupervised Pre-Training—0
Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models—0
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
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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