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 5101–5125 of 10307 papers

TitleStatusHype
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
Public Parking Spot Detection And Geo-localization Using Transfer Learning—0
PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction—0
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning—0
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories—0
Pushing the Limits of AMR Parsing with Self-Learning—0
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization—0
QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images—0
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model—0
Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis—0
Quality Diversity for Visual Pre-Training—0
Quality Estimation Using Dual Encoders with Transfer Learning—0
Quality In, Quality Out: Learning from Actual Mistakes—0
Quality versus Quantity: Building Catalan-English MT Resources—0
Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting—0
Quantifying Knowledge Distillation Using Partial Information Decomposition—0
Quantifying the Performance of Federated Transfer Learning—0
Quantifying the value of information transfer in population-based SHM—0
Quantifying the value of positive transfer: An experimental case study—0
Quantum Federated Learning With Quantum Networks—0
Quantum median filter for Total Variation image denoising—0
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes—0
Quantum Transfer Learning for Acceptability Judgements—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