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 8851–8875 of 10307 papers

TitleStatusHype
Augmenting Biomedical Named Entity Recognition with General-domain ResourcesCode0
Augmenting Knowledge Transfer across GraphsCode0
Augmenting semantic lexicons using word embeddings and transfer learningCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
A Unified Framework for Domain Adaptation using Metric Learning on ManifoldsCode0
A Unified Meta-Learning Framework for Dynamic Transfer LearningCode0
A Unified Neural Architecture for Instrumental Audio TasksCode0
OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption SpectraCode0
AutoFCL: Automatically Tuning Fully Connected Layers for Handling Small DatasetCode0
Automated Behavioral Analysis Using Instance SegmentationCode0
Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident LearningCode0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
Automated Long Answer Grading with RiceChem DatasetCode0
Automated Source Code Generation and Auto-completion Using Deep Learning: Comparing and Discussing Current Language-Model-Related ApproachesCode0
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representationsCode0
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques: A Comparative AnalysisCode0
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning TechniquesCode0
Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning TechniquesCode0
Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System ImagesCode0
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methodsCode0
Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural NetworksCode0
Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networksCode0
Automatic Issue Classifier: A Transfer Learning Framework for Classifying Issue ReportsCode0
Automatic location detection based on deep learningCode0
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide SurveyCode0
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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