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 7126–7150 of 10307 papers

TitleStatusHype
Multilingual Pre-Trained Transformers and Convolutional NN Classification Models for Technical Domain Identification—0
Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning—0
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic trainingCode0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Few-Shot Bayesian Optimization with Deep Kernel Surrogates—0
Predicting Pneumonia and Region Detection from X-Ray Images using Deep Neural Network—0
Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography—0
TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection—0
Transferring model structure in Bayesian transfer learning for Gaussian process regression—0
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates—0
A relic sketch extraction framework based on detail-aware hierarchical deep network—0
Supervised Transfer Learning at Scale for Medical Imaging—0
Self-Supervised Learning for Segmentation—0
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis—0
Hostility Detection in Hindi leveraging Pre-Trained Language ModelsCode0
Deep learning based prediction of Alzheimer's disease from magnetic resonance images—0
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
NetCut: Real-Time DNN Inference Using Layer Removal—0
Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand—0
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation—0
A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures—0
Action Priors for Large Action Spaces in RoboticsCode0
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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