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 3951–3975 of 10307 papers

TitleStatusHype
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—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
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—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
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Adaptive Transfer Learning for Plant Phenotyping—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
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French—0
Federated learning: Applications, challenges and future directions—0
From Limited Labels to Open Domains: An Efficient Learning Paradigm for UAV-view Geo-Localization—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