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 2501–2525 of 10307 papers

TitleStatusHype
Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators—0
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling—0
Tracking Universal Features Through Fine-Tuning and Model Merging—0
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System—0
TAS: Distilling Arbitrary Teacher and Student via a Hybrid Assistant—0
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning—0
YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection—0
Transfer Learning Adapts to Changing PSD in Gravitational Wave Data—0
Learning to rumble: Automated elephant call classification, detection and endpointing using deep architectures—0
Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis—0
Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations—0
Exploring transfer learning for Deep NLP systems on rarely annotated languages—0
A Survey on Deep Tabular Learning—0
Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention—0
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples—0
Model-based Large Language Model Customization as Service—0
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge TransferCode0
EEGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training—0
TL-PCA: Transfer Learning of Principal Component Analysis—0
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework—0
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
Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients—0
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal—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