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 9176–9200 of 10307 papers

TitleStatusHype
Close Yet Distinctive Domain Adaptation—0
Question answering using deep learning in low resource Indian language Marathi—0
CloudifierNet -- Deep Vision Models for Artificial Image Processing—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies—0
CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures—0
ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks—0
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction—0
Clustering Markov Decision Processes For Continual Transfer—0
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets—0
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer—0
CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter—0
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—0
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database—0
CNN Based Detection of Cardiovascular Diseases from ECG Images—0
CNN Based Flank Predictor for Quadruped Animal Species—0
Adaptive Sparse Transformer for Multilingual Translation—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