SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 5901–5950 of 10307 papers

TitleStatusHype
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference—0
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling—0
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?Code0
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training—0
YANMTT: Yet Another Neural Machine Translation Toolkit—0
TransFER: Learning Relation-aware Facial Expression Representations with Transformers—0
Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and PostsCode0
Making Person Search Enjoy the Merits of Person Re-identification—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
EEG-based Classification of Drivers Attention using Convolutional Neural Network—0
Web image search engine based on LSH index and CNN Resnet50—0
Frozen Pretrained Transformers for Neural Sign Language TranslationCode1
Plug and Play, Model-Based Reinforcement Learning—0
Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks—0
How Hateful are Movies? A Study and Prediction on Movie SubtitlesCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
A Multi-input Multi-output Transformer-based Hybrid Neural Network for Multi-class Privacy Disclosure Detection—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Do Vision Transformers See Like Convolutional Neural Networks?Code1
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Multimodal Knowledge Learning for Named Entity Disambiguation—0
Reliability and Robustness of Transformers for Automated Short-Answer Grading—0
KITTI-CARLA: a KITTI-like dataset generated by CARLA SimulatorCode1
CaT: Weakly Supervised Object Detection with Category TransferCode0
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemicCode0
On the Opportunities and Risks of Foundation ModelsCode1
Challenges for cognitive decoding using deep learning methods—0
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic SegmentationCode1
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples—0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language ModelsCode1
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—0
Focus on the Positives: Self-Supervised Learning for Biodiversity MonitoringCode0
Transfer Learning from an Artificial Radiograph-landmark Dataset for Registration of the Anatomic Skull Model to Dual Fluoroscopic X-ray Images—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
MTG: A Benchmark Suite for Multilingual Text GenerationCode0
One-shot Transfer Learning for Population MappingCode0
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image AnalysisCode1
TVT: Transferable Vision Transformer for Unsupervised Domain AdaptationCode1
Semantic Concentration for Domain AdaptationCode1
Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought—0
Deep Learning Classification of Lake ZooplanktonCode0
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?—0
Hand Pose Classification Based on Neural Networks—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified