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 9526–9550 of 10307 papers

TitleStatusHype
Count, Decode and Fetch: A New Approach to Handwritten Chinese Character Error Correction—0
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—0
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation—0
Recognizing License Plates in Real-Time—0
Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning—0
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks—0
COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data—0
COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs—0
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images—0
Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks—0
Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence—0
Recognizing Material Properties from Images—0
COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images—0
COVID-19 Detection using Transfer Learning with Convolutional Neural Network—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
COVID 19 Diagnosis Analysis using Transfer Learning—0
COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images—0
COVID-19 therapy target discovery with context-aware literature mining—0
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias—0
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19—0
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches—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