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 8751–8800 of 10307 papers

TitleStatusHype
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?—0
Curriculum Learning for Cumulative Return MaximizationCode0
Cross-View Policy Learning for Street NavigationCode0
The iMaterialist Fashion Attribute DatasetCode0
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking DatasetsCode1
UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles—0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction—0
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning—0
Recognizing License Plates in Real-Time—0
Stability of Graph Scattering TransformsCode0
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images—0
Transfer Learning for Ultrasound Tongue Contour Extraction with Different Domains—0
The role of ego vision in view-invariant action recognition—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
Transfer Learning by Modeling a Distribution over Policies—0
Forward and Backward Knowledge Transfer for Sentiment Classification—0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
Disentangled State Space Representations—0
Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier—0
Energy Predictive Models with Limited Data using Transfer Learning—0
Prediction of Workplace Injuries—0
A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging—0
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition—0
Transfer Learning with intelligent training data selection for prediction of Alzheimer's DiseaseCode0
What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNsCode1
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications—0
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid—0
An Adaptive Random Path Selection Approach for Incremental LearningCode0
Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation—0
Continual learning with hypernetworksCode1
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset—0
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain AdaptationCode0
Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
Cross-lingual Transfer Learning for Japanese Named Entity Recognition—0
Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection—0
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data—0
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning ApproachesCode0
MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection—0
EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination—0
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers—0
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection—0
WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining—0
Transfer Learning in Natural Language Processing—0
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator NetworkCode0
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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