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 8101–8125 of 10307 papers

TitleStatusHype
Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique—0
Within the Lack of COVID-19 Benchmark Dataset: A Novel GAN with Deep Transfer Learning for Corona-virus Detection in Chest X-ray Images—0
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation—0
Deep Face Forgery DetectionCode0
Light3DPose: Real-time Multi-Person 3D PoseEstimation from Multiple Views—0
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited DevicesCode0
Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks—0
Any-Shot Sequential Anomaly Detection in Surveillance Videos—0
TAPAS: Weakly Supervised Table Parsing via Pre-trainingCode0
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment AnalysisCode0
Deep learning approaches in food recognitionCode0
Complete CVDL Methodology for Investigating Hydrodynamic InstabilitiesCode0
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification—0
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology—0
On-Device Transfer Learning for Personalising Psychological Stress Modelling using a Convolutional Neural Network—0
Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset—0
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations—0
Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata—0
Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems—0
Deep Learning Based Multi-Label Text Classification of UNGA ResolutionsCode0
Pose-Guided Knowledge Transfer for Object Part Segmentation—0
Adversarial Transfer Learning for Punctuation Restoration—0
Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms—0
Analysis of Knowledge Transfer in Kernel Regime—0
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks—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