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 101–110 of 10307 papers

TitleStatusHype
Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better—0
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data—0
When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?Code0
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective—0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
Intelligent Incident Hypertension Prediction in Obstructive Sleep Apnea—0
Transfer learning for multifidelity simulation-based inference in cosmology—0
Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off—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