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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4976–5000 of 10307 papers

TitleStatusHype
Precipitation Nowcasting With Spatial And Temporal Transfer Learning Using Swin-UNETR—0
Precise Knowledge Transfer via Flow Matching—0
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network—0
Predicting engagement in online social networks: Challenges and opportunities—0
Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking—0
Predicting Foreign Language Usage from English-Only Social Media Posts—0
Predicting Helpfulness of Online Reviews—0
Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis—0
Predicting Lung Disease Severity via Image-Based AQI Analysis using Deep Learning Techniques—0
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning—0
Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks—0
Predicting Pneumonia and Region Detection from X-Ray Images using Deep Neural Network—0
Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks—0
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning—0
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks—0
Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input—0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information—0
Predicting the Difficulty and Response Time of Multiple Choice Questions Using Transfer Learning—0
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning—0
Predicting the real-valued distances between residue pairs for proteins—0
Predicting the Success of Domain Adaptation in Text Similarity—0
Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach—0
Predicting User Roles in Social Networks using Transfer Learning with Feature Transformation—0
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities—0
Predicting with Proxies: Transfer Learning in High Dimension—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