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 3976–4000 of 10307 papers

TitleStatusHype
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—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
Composing Task-Agnostic Policies with Deep Reinforcement Learning—0
Federated Multi-View Synthesizing for Metaverse—0
Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System—0
Federated Deep Reinforcement Learning—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer—0
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks—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
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Federated Transfer Learning with Dynamic Gradient Aggregation—0
Adaptive Transfer Learning for Plant Phenotyping—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
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch—0
G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning—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