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 2526–2550 of 10307 papers

TitleStatusHype
Features are fate: a theory of transfer learning in high-dimensional regression—0
CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected EnvironmentCode0
More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed RoutingCode0
Unsupervised Data Validation Methods for Efficient Model Training—0
Federated Graph Learning for Cross-Domain Recommendation—0
Non-transferable Pruning—0
Robustness and Security Enhancement of Radio Frequency Fingerprint Identification in Time-Varying Channels—0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
Z-upscaling: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM VolumesCode0
Collusion Detection with Graph Neural Networks—0
Transfer Learning for a Class of Cascade Dynamical Systems—0
Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled DataCode0
Seg2Act: Global Context-aware Action Generation for Document Logical StructuringCode0
Utilizing Transfer Learning and pre-trained Models for Effective Forest Fire Detection: A Case Study of Uttarakhand—0
rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG—0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery—0
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning—0
ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models—0
Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior—0
Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models—0
Pre-Ictal Seizure Prediction Using Personalized Deep Learning—0
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data—0
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning—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