SOTAVerified

Unsupervised Pre-training

Pre-training a neural network using unsupervised (self-supervised) auxiliary tasks on unlabeled data.

Papers

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Showing 101–150 of 265 papers

TitleStatusHype
Self-training and Pre-training are Complementary for Speech RecognitionCode0
Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-IdentificationCode0
Knowledge Matters: Importance of Prior Information for OptimizationCode0
Post Training in Deep Learning with Last KernelCode0
Functional Regularization for Representation Learning: A Unified Theoretical PerspectiveCode0
Calibrating Language Models with Adaptive Temperature ScalingCode0
From Recognition to Prediction: Leveraging Sequence Reasoning for Action AnticipationCode0
GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection MethodCode0
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug DiscoveryCode0
A Large-Scale Study on Unsupervised Spatiotemporal Representation LearningCode0
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot MethodCode0
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic DataCode0
Greedy-layer Pruning: Speeding up Transformer Models for Natural Language ProcessingCode0
SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual RepresentationsCode0
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task LearningCode0
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question AnsweringCode0
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health RecordsCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
How far can we go without convolution: Improving fully-connected networksCode0
How much do LLMs learn from negative examples?Code0
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Learning of feature points without additional supervision improves reinforcement learning from imagesCode0
Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance SegmentationCode0
Unsupervised Learning with Truncated Gaussian Graphical Models—0
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data—0
Unsupervised pre-training for sequence to sequence speech recognition—0
Unsupervised Pre-Training for 3D Leaf Instance Segmentation—0
Unsupervised Pre-training for Biomedical Question Answering—0
Unsupervised Pre-training for Natural Language Generation: A Literature Review—0
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors—0
Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project—0
Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project—0
Unsupervised pre-training helps to conserve views from input distribution—0
Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis—0
Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models—0
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference—0
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain—0
Weakly Supervised Construction of ASR Systems with Massive Video Data—0
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?—0
What Makes for Good Views for Contrastive Learning?—0
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification—0
3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents—0
A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology—0
Adversarial Ladder Networks—0
An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS—0
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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