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 3226–3250 of 10307 papers

TitleStatusHype
Dropping Networks for Transfer Learning—0
Drowsiness Detection Based On Driver Temporal Behavior Using a New Developed Dataset—0
DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models—0
Automatic segmentation of texts into units of meaning for reading assistance—0
Drug repositioning for Alzheimer's disease with transfer learning—0
Energy efficient distributed analytics at the edge of the network for IoT environments—0
DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion—0
Building and Road Segmentation Using EffUNet and Transfer Learning Approach—0
Novel coronavirus pneumonia lesion segmentation in CT images—0
Analysis and Prediction of NLP models via Task Embeddings—0
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning—0
DT-LET: Deep Transfer Learning by Exploring where to Transfer—0
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach—0
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors—0
DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis—0
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning—0
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis—0
Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy—0
Dual-Correction Adaptation Network for Noisy Knowledge Transfer—0
Dual Decomposition of Weights and Singular Value Low Rank Adaptation—0
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks—0
Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy—0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images—0
Deep Learning Approach Combining Lightweight CNN Architecture with Transfer Learning: An Automatic Approach for the Detection and Recognition of Bangladeshi Banknotes—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