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 8826–8850 of 10307 papers

TitleStatusHype
AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image SegmentationCode0
A Study of Convolutional Architectures for Handshape Recognition applied to Sign LanguageCode0
A Survey of Available Corpora for Building Data-Driven Dialogue SystemsCode0
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter CollaborationCode0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
A Survey on Deep Learning of Small Sample in Biomedical Image AnalysisCode0
A Survey on Prompt TuningCode0
Asymmetric Co-Training for Source-Free Few-Shot Domain AdaptationCode0
Asynchronous Multi-Task LearningCode0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer LearningCode0
A Technical Question Answering System with Transfer LearningCode0
ATL: Autonomous Knowledge Transfer from Many Streaming ProcessesCode0
A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health EstimationCode0
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue CommentsCode0
A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio dataCode0
A transfer learning metamodel using artificial neural networks applied to natural convection flows in enclosuresCode0
Attend Before you Act: Leveraging human visual attention for continual learningCode0
Two-Level Attention-based Fusion Learning for RGB-D Face RecognitionCode0
Attention Based Fully Convolutional Network for Speech Emotion RecognitionCode0
Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency TransferCode0
Attentive Multi-Task Deep Reinforcement LearningCode0
A Tulu Resource for Machine TranslationCode0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
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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