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 9076–9100 of 10307 papers

TitleStatusHype
Cross-lingual Intermediate Fine-tuning improves Dialogue State TrackingCode0
Cross-Lingual Knowledge Transfer for Clinical PhenotypingCode0
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in TurkishCode0
Cross-lingual Lifelong LearningCode0
Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of MarathiCode0
Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and ChallengesCode0
Cross-lingual sentiment classification in low-resource Bengali languageCode0
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource LanguageCode0
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification GraphsCode0
Cross Modality Knowledge Distillation for Multi-Modal Aerial View Object ClassificationCode0
Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video DetectionCode0
Cross-project Defect Prediction with An Enhanced Transfer Boosting AlgorithmCode0
Cross-View Policy Learning for Street NavigationCode0
CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report SummarizationCode0
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion RecognitionCode0
CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head RedirectionCode0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image SegmentationCode0
Curriculum Learning for Cumulative Return MaximizationCode0
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image ClassificationCode0
CycDA: Unsupervised Cycle Domain Adaptation from Image to VideoCode0
Cycle-consistent Conditional Adversarial Transfer NetworksCode0
Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object DetectionCode0
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain RandomizationCode0
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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