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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 201–250 of 265 papers

TitleStatusHype
Author2Vec: A Framework for Generating User Embedding—0
Transfer Learning for Context-Aware Spoken Language Understanding—0
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data—0
Synthetic vascular structure generation for unsupervised pre-training in CTA segmentation tasks—0
Unsupervised Pre-training for Natural Language Generation: A Literature Review—0
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug DiscoveryCode0
MML: Maximal Multiverse Learning for Robust Fine-Tuning of Language ModelsCode0
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification—0
Empirical Evaluation of Active Learning Techniques for Neural MT—0
Recognizing UMLS Semantic Types with Deep Learning—0
Extractive NarrativeQA with Heuristic Pre-Training—0
Unsupervised pre-training for sequence to sequence speech recognition—0
Pre-train and Learn: Preserve Global Information for Graph Neural NetworksCode0
Improving Transformer-based Speech Recognition Using Unsupervised Pre-trainingCode1
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models—0
SMiRL: Surprise Minimizing RL in Entropic Environments—0
Understanding Semantics from Speech Through Pre-training—0
Bridging the domain gap in cross-lingual document classificationCode0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Cross-Domain Training for Goal-Oriented Conversational Agents—0
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic DataCode0
Tuning Multilingual Transformers for Language-Specific Named Entity RecognitionCode0
Leveraging Pre-trained Checkpoints for Sequence Generation TasksCode1
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
Improving Relation Extraction by Pre-trained Language RepresentationsCode0
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