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 1701–1725 of 10307 papers

TitleStatusHype
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge TransferCode0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing—0
MoKD: Multi-Task Optimization for Knowledge Distillation—0
GNN-based Precoder Design and Fine-tuning for Cell-free Massive MIMO with Real-world CSI—0
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer—0
A computer vision-based model for occupancy detection using low-resolution thermal images—0
Revealing economic facts: LLMs know more than they say—0
Sleep Position Classification using Transfer Learning for Bed-based Pressure Sensors—0
Transfer Learning Across Fixed-Income Product Classes—0
Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies—0
Gameplay Highlights Generation—0
Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)—0
Linux Kernel Configurations at Scale: A Dataset for Performance and Evolution AnalysisCode0
A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource SettingCode0
Bridging Ears and Eyes: Analyzing Audio and Visual Large Language Models to Humans in Visible Sound Recognition and Reducing Their Sensory Gap via Cross-Modal Distillation—0
A systematic review of challenges and proposed solutions in modeling multimodal data—0
Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations—0
Development of a WAZOBIA-Named Entity Recognition System—0
Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical ApplicationsCode0
Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment—0
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly PipelinesCode0
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review—0
HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder—0
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal—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