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 8776–8800 of 10307 papers

TitleStatusHype
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
A Deep Generative Framework for Joint Households and Individuals Population Synthesis—0
Predicting with Proxies: Transfer Learning in High Dimension—0
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition—0
A bandit approach to curriculum generation for automatic speech recognition—0
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Prediction of clinical tremor severity using Rank Consistent Ordinal Regression—0
Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning—0
Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning—0
Prediction of the Most Fire-Sensitive Point in Building Structures with Differentiable Agents for Thermal Simulators—0
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks—0
Prediction of Workplace Injuries—0
Predictive Analysis of Diabetic Retinopathy with Transfer Learning—0
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach—0
Predictive modeling of brain tumor: A Deep learning approach—0
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks—0
Predictive Models from Quantum Computer Benchmarks—0
Predictor Combination at Test Time—0
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
Supervised Transfer Learning at Scale for Medical Imaging—0
Pre-Ictal Seizure Prediction Using Personalized Deep Learning—0
Preliminary Steps Towards Federated Sentiment Classification—0
Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning—0
Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation For Action Recognition—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