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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7626–7650 of 10307 papers

TitleStatusHype
Transfer learning of phase transitions in percolation and directed percolation—0
Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata—0
Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems—0
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations—0
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks—0
Transfer Learning of Tabular Data by Finetuning Large Language Models—0
Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak—0
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond—0
Transfer Learning on Manifolds via Learned Transport Operators—0
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling—0
Transfer Learning on Multi-Fidelity Data—0
Transfer Learning on Transformers for Building Energy Consumption Forecasting -- A Comparative Study—0
Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction—0
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms—0
TransFER: Learning Relation-aware Facial Expression Representations with Transformers—0
Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification—0
Transfer Learning Study of Motion Transformer-based Trajectory Predictions—0
Suppressing simulation bias using multi-modal data—0
Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images—0
Transfer learning to decode brain states reflecting the relationship between cognitive tasks—0
Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models—0
Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values—0
Transfer learning to improve streamflow forecasts in data sparse regions—0
Transfer Learning to Learn with Multitask Neural Model Search—0
Transfer learning to model inertial confinement fusion experiments—0
Show:102550
← PrevPage 306 of 413Next →
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