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 4426–4450 of 10307 papers

TitleStatusHype
Transfer Learning from Partial Annotations for Whole Brain Segmentation—0
Transfer Learning from Pre-trained BERT for Pronoun Resolution—0
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization—0
Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection—0
Transfer Learning From Sound Representations For Anger Detection in Speech—0
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod—0
Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection—0
Transfer learning from synthetic to real images using variational autoencoders for robotic applications—0
Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task—0
Transfer Learning from Whisper for Microscopic Intelligibility Prediction—0
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations—0
Transfer Learning Guided Noise Reduction for Automatic Modulation Classification—0
Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022—0
Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients—0
Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging—0
Transfer Learning in Astronomy: A New Machine-Learning Paradigm—0
Transfer Learning in a Transductive Setting—0
Transfer Learning in Bandits with Latent Continuity—0
Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task—0
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders—0
Transfer Learning in CNNs Using Filter-Trees—0
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Transfer Learning in Deep Reinforcement Learning: A Survey—0
Transfer Learning In Differential Privacy's Hybrid-Model—0
Transfer Learning in Electronic Health Records through Clinical Concept Embedding—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