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 8726–8750 of 10307 papers

TitleStatusHype
POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT Scans: A Comparison of Deep Learning Models—0
Supervised Contrastive Learning for Cross-lingual Transfer Learning—0
A Deep Learning Architectures for Kidney Disease Classification—0
Post-Transfer Learning Statistical Inference in High-Dimensional Regression—0
Posture recognition using an RGB-D camera : exploring 3D body modeling and deep learning approaches—0
PotatoPestNet: A CTInceptionV3-RS-Based Neural Network for Accurate Identification of Potato Pests—0
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems—0
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks—0
PowerGraph: Using neural networks and principal components to determine multivariate statistical power trade-offs—0
Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System—0
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram—0
PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training—0
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data—0
Practical and sample efficient zero-shot HPO—0
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification—0
Practical Insights into Knowledge Distillation for Pre-Trained Models—0
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning—0
Practical Semantic Parsing for Spoken Language Understanding—0
Practical Transferability Estimation for Image Classification Tasks—0
PRADO: Projection Attention Networks for Document Classification On-Device—0
PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency—0
Precipitation Nowcasting With Spatial And Temporal Transfer Learning Using Swin-UNETR—0
Precise Knowledge Transfer via Flow Matching—0
A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation—0
A Base Model Selection Methodology for Efficient Fine-Tuning—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