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
Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning—0
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation—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
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance—0
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—0
Evaluating Pixel Language Models on Non-Standardized Languages—0
Federated deep transfer learning for EEG decoding using multiple BCI tasks—0
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—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
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data—0
Active Multitask Learning with Committees—0
Federated Graph Learning with Graphless Clients—0
Accelerating Multi-Model Inference by Merging DNNs of Different Weights—0
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Evaluating Knowledge Transfer in Neural Network for Medical Images—0
Federated learning: Applications, challenges and future directions—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—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