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 7451–7475 of 10307 papers

TitleStatusHype
MedDialog: Large-scale Medical Dialogue DatasetsCode0
CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets—0
The Amazing World of Neural Language Generation—0
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer—0
Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation—0
CUNI Submission for the Inuktitut Language in WMT News 2020—0
Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition—0
The NiuTrans System for the WMT20 Quality Estimation Shared Task—0
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey—0
WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments—0
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question AnsweringCode0
Transfer Learning for Related Languages: Submissions to the WMT20 Similar Language Translation Task—0
Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning—0
UET at WNUT-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with RoBERTa—0
Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System ImagesCode0
Is Transfer Learning Necessary for Protein Landscape Prediction?—0
Free the Plural: Unrestricted Split-Antecedent Anaphora ResolutionCode0
Multimodal and self-supervised representation learning for automatic gesture recognition in surgical robotics—0
Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19—0
C-Net: A Reliable Convolutional Neural Network for Biomedical Image ClassificationCode0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—0
Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks—0
Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients—0
Polymer Informatics with Multi-Task Learning—0
Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth—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