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 3101–3125 of 10307 papers

TitleStatusHype
DocumentNet: Bridging the Data Gap in Document Pre-Training—0
Boosting offline handwritten text recognition in historical documents with few labeled lines—0
Deep Learning-based Extreme Heatwave Forecast—0
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users—0
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition—0
Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection—0
Does Adversarial Transferability Indicate Knowledge Transferability?—0
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP—0
Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation—0
How Does an Approximate Model Help in Reinforcement Learning?—0
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning—0
Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation—0
Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records—0
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data—0
Does Robustness on ImageNet Transfer to Downstream Tasks?—0
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study—0
Driver Safety Development Real Time Driver Drowsiness Detection System Based on Convolutional Neural Network—0
Dog Identification using Soft Biometrics and Neural Networks—0
Automatic Tuberculosis and COVID-19 cough classification using deep learning—0
Deep Learning-Based Communication Over the Air—0
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning—0
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation—0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Domain Adaptation and Transfer Learning in StochasticNets—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—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