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 10001–10050 of 10307 papers

TitleStatusHype
Feature Space Transfer for Data Augmentation—0
Feature-Supervised Action Modality Transfer—0
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data—0
Feature Transfer Learning for Face Recognition With Under-Represented Data—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy—0
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning—0
Evaluation of Federated Learning in Phishing Email Detection—0
Federated Adversarial Domain Adaptation—0
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms—0
Federated and Transfer Learning for Cancer Detection Based on Image Analysis—0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Federated deep transfer learning for EEG decoding using multiple BCI tasks—0
Federated Distillation: A Survey—0
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data—0
Federated Graph Learning for Cross-Domain Recommendation—0
Federated Graph Learning with Graphless Clients—0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—0
Federated learning: Applications, challenges and future directions—0
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
Federated Learning for Spoken Language Understanding—0
Federated Learning -- Methods, Applications and beyond—0
Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks—0
Federated Learning without Full Labels: A Survey—0
Federated Multi-View Synthesizing for Metaverse—0
Federated Deep Reinforcement Learning—0
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence—0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer—0
Federated Transfer Component Analysis Towards Effective VNF Profiling—0
Federated Transfer Learning Aided Interference Classification in GNSS Signals—0
Federated Transfer Learning Based Cooperative Wideband Spectrum Sensing with Model Pruning—0
Federated Transfer Learning: concept and applications—0
Federated Transfer Learning with Differential Privacy—0
Federated Transfer Learning with Dynamic Gradient Aggregation—0
Federated Transfer Learning with Multimodal Data—0
Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding—0
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces—0
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning—0
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare—0
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare—0
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge—0
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning—0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—0
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities—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