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 2001–2025 of 10307 papers

TitleStatusHype
Transfer Learning for T-Cell Response PredictionCode0
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules—0
Transfer Learning Beyond Bounded Density Ratios—0
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attentionCode1
MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging TasksCode0
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes—0
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition—0
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk—0
Federated Transfer Learning with Differential Privacy—0
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to RCode0
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval—0
Automatic location detection based on deep learningCode0
LuoJiaHOG: A Hierarchy Oriented Geo-aware Image Caption Dataset for Remote Sensing Image-Text Retrival—0
On the low-shot transferability of [V]-Mamba—0
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning—0
Open Continual Feature Selection via Granular-Ball Knowledge TransferCode0
TransLandSeg: A Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model—0
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical ImagesCode1
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment—0
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models—0
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D UnderstandingCode1
SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration—0
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—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