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 1–25 of 10307 papers

TitleStatusHype
RaMen: Multi-Strategy Multi-Modal Learning for Bundle ConstructionCode0
Disentangling coincident cell events using deep transfer learning and compressive sensing—0
Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning WorkflowsCode0
Robust-Multi-Task Gradient BoostingCode0
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift—0
The Bayesian Approach to Continual Learning: An Overview—0
A Survey on Prompt TuningCode0
Agent KB: Leveraging Cross-Domain Experience for Agentic Problem SolvingCode3
DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data AugmentationCode0
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer LearningCode0
Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol—0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
High-Order Deep Meta-Learning with Category-Theoretic Interpretation—0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature AlignmentCode1
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Distilling Normalizing Flows—0
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset EvaluationCode0
Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher—0
FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment—0
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion—0
Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning—0
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach—0
From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data—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