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 1076–1100 of 10307 papers

TitleStatusHype
YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection—0
EEGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training—0
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples—0
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework—0
Model-based Large Language Model Customization as Service—0
TL-PCA: Transfer Learning of Principal Component Analysis—0
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge TransferCode0
Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text RecognitionCode1
Magnituder Layers for Implicit Neural Representations in 3D—0
Deep Transfer Learning: Model Framework and Error Analysis—0
Hey AI Can You Grade My Essay?: Automatic Essay Grading—0
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal—0
Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients—0
Robustness and Security Enhancement of Radio Frequency Fingerprint Identification in Time-Varying Channels—0
Federated Graph Learning for Cross-Domain Recommendation—0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed RoutingCode0
Unsupervised Data Validation Methods for Efficient Model Training—0
CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected EnvironmentCode0
Features are fate: a theory of transfer learning in high-dimensional regression—0
Non-transferable Pruning—0
rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG—0
Seg2Act: Global Context-aware Action Generation for Document Logical StructuringCode0
Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled DataCode0
Utilizing Transfer Learning and pre-trained Models for Effective Forest Fire Detection: A Case Study of Uttarakhand—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