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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8401–8425 of 10307 papers

TitleStatusHype
Unleashing the Potential of Synthetic Images: A Study on Histopathology Image ClassificationCode0
Transfer Learning with Informative Priors: Simple Baselines Better than Previously ReportedCode0
Transfer Learning with intelligent training data selection for prediction of Alzheimer's DiseaseCode0
Transfer Learning Toolkit: Primers and BenchmarksCode0
Transfer Learning Robustness in Multi-Class Categorization by Fine-Tuning Pre-Trained Contextualized Language ModelsCode0
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image SegmentationCode0
Transfer Learning Between Related Tasks Using Expected Label ProportionsCode0
Towards a Simple Framework of Skill Transfer Learning for Robotic Ultrasound-guidance ProceduresCode0
TinySubNets: An efficient and low capacity continual learning strategyCode0
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched FlowsCode0
Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of DatasetsCode0
Toward Comprehensive Understanding of a Sentiment Based on Human MotivesCode0
Transfer Learning of RSSI to Improve Indoor Localisation PerformanceCode0
Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine TranslationCode0
Transfer Learning with Reconstruction LossCode0
Transfer Learning with Self-Supervised Vision Transformers for Snake IdentificationCode0
Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall ClassificationCode0
Transfer Learning with Shallow Decoders: BSC at WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared TaskCode0
XL-NBT: A Cross-lingual Neural Belief Tracking FrameworkCode0
Transfer Learning with Synthetic Corpora for Spatial Role Labeling and ReasoningCode0
Weaponizing Unicodes with Deep Learning -- Identifying Homoglyphs with Weakly Labeled DataCode0
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State TrackingCode0
Transfer and Multi-Task Learning for Noun-Noun Compound InterpretationCode0
Transfer and Alignment Network for Generalized Category DiscoveryCode0
Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral MappingCode0
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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