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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4001–4025 of 10307 papers

TitleStatusHype
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition—0
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning—0
Active Multitask Learning with Committees—0
Accelerating Multi-Model Inference by Merging DNNs of Different Weights—0
Evaluating Knowledge Transfer in Neural Network for Medical Images—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
A Probabilistic Approach to Knowledge Translation—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
How to Not Measure Disentanglement—0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation—0
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models—0
Evaluating and Improving Child-Directed Automatic Speech Recognition—0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers—0
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules—0
Few-shot Adaptive Object Detection with Cross-Domain CutMix—0
Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs—0
Class Subset Selection for Transfer Learning using Submodularity—0
\'Etude de l'apprentissage par transfert de syst\`emes de traduction automatique neuronaux (Study on transfer learning in neural machine translation )—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