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 2801–2825 of 10307 papers

TitleStatusHype
Deep Learning for Computational Cytology: A Survey—0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
Auto-Transfer: Learning to Route Transferable Representations—0
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification—0
Deep Transfer Learning for Person Re-identification—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Deep Transfer Learning For Plant Center Localization—0
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications—0
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch—0
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data—0
Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables—0
Deep transfer learning for system identification using long short-term memory neural networks—0
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology—0
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Deep Transfer Learning For Whole-Brain fMRI Analyses—0
Deep Transfer Learning for WiFi Localization—0
Bayesian Optimization of Bilevel Problems—0
Deep Learning for Bias Detection: From Inception to Deployment—0
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images—0
Deep Transfer Learning: Model Framework and Error Analysis—0
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations—0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition—0
Deep Transfer Learning with Joint Adaptation Networks—0
How Does an Approximate Model Help in Reinforcement Learning?—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