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 7501–7525 of 10307 papers

TitleStatusHype
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016—0
Transfer Learning for Efficient Iterative Safety Validation—0
Transfer Learning for Endoscopic Image Classification—0
Transfer learning for ensembles: reducing computation time and keeping the diversity—0
Transfer Learning for Estimating Causal Effects using Neural Networks—0
Transfer Learning for Estimation of Pendubot Angular Position Using Deep Neural Networks—0
Transfer Learning for Fault Diagnosis of Transmission Lines—0
Transfer learning for financial data predictions: a systematic review—0
Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers—0
Transfer Learning for Finetuning Large Language Models—0
Transfer Learning for Future Wireless Networks: A Comprehensive Survey—0
Transfer Learning for High-dimensional Quantile Regression with Distribution Shift—0
Transfer Learning for High-dimensional Reduced Rank Time Series Models—0
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks—0
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations—0
Transfer Learning for Information Extraction with Limited Data—0
Transfer Learning for Input Estimation of Vehicle Systems—0
Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm—0
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images—0
Transfer Learning for Latent Variable Network Models—0
Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic—0
Transfer Learning for Linear Regression: a Statistical Test of Gain—0
Transfer Learning for LQR Control—0
Transfer Learning for Material Classification using Convolutional Networks—0
Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge—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