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 1526–1550 of 10307 papers

TitleStatusHype
Deep Hashing Network for Unsupervised Domain AdaptationCode1
Supervised Learning of Universal Sentence Representations from Natural Language Inference DataCode1
Transfer Learning for Sequence Tagging with Hierarchical Recurrent NetworksCode1
Unsupervised Learning of Visual Representations by Solving Jigsaw PuzzlesCode1
Bridge Correlational Neural Networks for Multilingual Multimodal Representation LearningCode1
Unsupervised Domain Adaptation by BackpropagationCode1
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
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
DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data AugmentationCode0
A Survey on Prompt TuningCode0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation—0
High-Order Deep Meta-Learning with Category-Theoretic Interpretation—0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset EvaluationCode0
Distilling Normalizing Flows—0
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach—0
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion—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