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 8051–8075 of 10307 papers

TitleStatusHype
Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization—0
Transfer Learning for sEMG-based Hand Gesture Classification using Deep Learning in a Master-Slave Architecture—0
Joint Liver Lesion Segmentation and Classification via Transfer Learning—0
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline—0
A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation—0
Development of a High Fidelity Simulator for Generalised Photometric Based Space Object Classification using Machine Learning—0
On the safety of vulnerable road users by cyclist orientation detection using Deep Learning—0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Data Annealing for Informal Language Understanding Tasks—0
How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?—0
Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning—0
A survey on domain adaptation theory: learning bounds and theoretical guarantees—0
Learning Constrained Dynamics with Gauss Principle adhering Gaussian ProcessesCode0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization—0
Deep Learning for Screening COVID-19 using Chest X-Ray Images—0
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning—0
Per-pixel Classification Rebar Exposures in Bridge Eye-inspection—0
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis—0
Unsupervised Person Re-identification via Multi-label Classification—0
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans—0
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans—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