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 7001–7025 of 10307 papers

TitleStatusHype
Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encodingCode0
NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil—0
Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study—0
Transfer Learning-Based Model Protection With Secret Key—0
Transfer Learning based Speech Affect Recognition in Urdu—0
Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection—0
Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learning—0
Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy—0
Deep Neural Network Models Compression—0
Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks—0
End-to-end acoustic modelling for phone recognition of young readers—0
A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models—0
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis—0
Learning Invariant Representations across Domains and Tasks—0
Domain Generalization: A Survey—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural NetworkCode0
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power linesCode0
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
Fast Adaptation with Linearized Neural Networks—0
Understanding WiFi Signal Frequency Features for Position-Independent Gesture Sensing—0
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning—0
The Relevance of the Source Language in Transfer Learning for ASR—0
A Machine Learning Approach for Predicting Human Preference for Graph Layouts—0
Deep Bag-of-Sub-Emotions for Depression Detection in Social Media—0
Show:102550
← PrevPage 281 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