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 3001–3025 of 10307 papers

TitleStatusHype
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS—0
Binary Paragraph Vectors—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
Direct mineral content prediction from drill core images via transfer learning—0
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing—0
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
Discovering Hidden Physics Behind Transport Dynamics—0
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning—0
Deep learning-based variational autoencoder for classification of quantum and classical states of light—0
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network—0
Autonomous crater detection on asteroids using a fully-convolutional neural network—0
Discriminative Label Consistent Domain Adaptation—0
Discriminative Partial Domain Adversarial Network—0
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation—0
Deep Learning-Based Transfer Learning for Classification of Cassava Disease—0
Discriminative Transfer Learning with Tree-based Priors—0
AutoML Systems For Medical Imaging—0
Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities—0
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Deep Learning-based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment—0
Show:102550
← PrevPage 121 of 413Next →
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