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 2341–2350 of 10307 papers

TitleStatusHype
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection—0
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies—0
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning—0
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding—0
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer—0
A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)—0
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery—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