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 4126–4150 of 10307 papers

TitleStatusHype
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Ensembles of Deep Neural Networks for Action Recognition in Still Images—0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis—0
Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning—0
Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema—0
Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection—0
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy—0
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation—0
Approximation by non-symmetric networks for cross-domain learning—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation—0
Enriching a Fashion Knowledge Graph from Product Textual Descriptions—0
Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning—0
Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning—0
Enhancing Visual Continual Learning with Language-Guided Supervision—0
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Approximated Prompt Tuning for Vision-Language Pre-trained Models—0
Active Learning for Rumor Identification on Social Media—0
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models—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