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
Evaluating Pixel Language Models on Non-Standardized Languages—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
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
Federated Multi-View Synthesizing for Metaverse—0
A probabilistic constrained clustering for transfer learning and image category discovery—0
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition—0
Active Multitask Learning with Committees—0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer—0
Accelerating Multi-Model Inference by Merging DNNs of Different Weights—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
Evaluating Knowledge Transfer in Neural Network for Medical Images—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
Federated Transfer Learning with Dynamic Gradient Aggregation—0
A Probabilistic Approach to Knowledge Translation—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
How to Not Measure Disentanglement—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—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