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 9426–9450 of 10307 papers

TitleStatusHype
Force myography benchmark data for hand gesture recognition and transfer learningCode0
Forecasting Future Humphrey Visual Fields Using Deep LearningCode0
Forecasting new diseases in low-data settings using transfer learningCode0
FOSI: Hybrid First and Second Order OptimizationCode0
Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior PredictionCode0
Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection ModelsCode0
Free the Plural: Unrestricted Split-Antecedent Anaphora ResolutionCode0
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning TechniquesCode0
From Colors to Classes: Emergence of Concepts in Vision TransformersCode0
From English to Code-Switching: Transfer Learning with Strong Morphological CluesCode0
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal ImagesCode0
From Video Game to Real Robot: The Transfer between Action SpacesCode0
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided DecodingCode0
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image ClassificationCode0
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural NetworksCode0
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformationsCode0
Functional Knowledge Transfer with Self-supervised Representation LearningCode0
Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text ClassificationCode0
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across TokenizersCode0
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust FusionCode0
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-ScansCode0
Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility AssessmentCode0
GAN Cocktail: mixing GANs without dataset accessCode0
GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack DetectionCode0
GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer LearningCode0
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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