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 6501–6525 of 10307 papers

TitleStatusHype
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks—0
Plug and Play, Model-Based Reinforcement Learning—0
Web image search engine based on LSH index and CNN Resnet50—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
A Multi-input Multi-output Transformer-based Hybrid Neural Network for Multi-class Privacy Disclosure Detection—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM—0
Multimodal Knowledge Learning for Named Entity Disambiguation—0
CaT: Weakly Supervised Object Detection with Category TransferCode0
Reliability and Robustness of Transformers for Automated Short-Answer Grading—0
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemicCode0
Challenges for cognitive decoding using deep learning methods—0
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples—0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Focus on the Positives: Self-Supervised Learning for Biodiversity MonitoringCode0
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—0
Transfer Learning from an Artificial Radiograph-landmark Dataset for Registration of the Anatomic Skull Model to Dual Fluoroscopic X-ray Images—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
One-shot Transfer Learning for Population MappingCode0
MTG: A Benchmark Suite for Multilingual Text GenerationCode0
Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought—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