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 7276–7300 of 10307 papers

TitleStatusHype
Text-to-speech for the hearing impaired—0
Sentiment analysis in Bengali via transfer learning using multi-lingual BERTCode0
Transfer Learning as an Enabler of the Intelligent Digital Twin—0
Chair Segments: A Compact Benchmark for the Study of Object SegmentationCode0
Empirical Study on the Software Engineering Practices in Open Source ML Package Repositories—0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes—0
Label Representations in Modeling Classification as Text Generation—0
Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer LearningCode0
Hierarchical Granularity Transfer Learning—0
Anaphoric Zero Pronoun Identification: A Multilingual Approach—0
SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models—0
NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation—0
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models—0
Federated Learning for Spoken Language Understanding—0
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction—0
Effective Few-Shot Classification with Transfer Learning—0
Offensive Language Detection on Video Live Streaming Chat—0
Cross-lingual Transfer Learning for Grammatical Error Correction—0
Classifying Judgements using Transfer Learning—0
Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning—0
KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug ReactionsCode0
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules—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