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 3951–3975 of 10307 papers

TitleStatusHype
Transferring climate change physical knowledgeCode0
SINCERE: Supervised Information Noise-Contrastive Estimation REvisitedCode0
Physics-Informed Solution of The Stationary Fokker-Plank Equation for a Class of Nonlinear Dynamical Systems: An Evaluation Study—0
Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions—0
Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model—0
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI—0
Cross-modal Alignment with Optimal Transport for CTC-based ASR—0
Crack-Net: Prediction of Crack Propagation in Composites—0
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition—0
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning—0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
Robust Navigation with Cross-Modal Fusion and Knowledge TransferCode0
Attention Is All You Need For Blind Room Volume Estimation—0
STemGAN: spatio-temporal generative adversarial network for video anomaly detection—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts—0
Multiply Robust Federated Estimation of Targeted Average Treatment Effects—0
Identification of pneumonia on chest x-ray images through machine learning—0
Brain Tumor Detection Using Deep Learning Approaches—0
Using Artificial Intelligence for the Automation of Knitting Patterns—0
Hand Gesture Recognition with Two Stage Approach Using Transfer Learning and Deep Ensemble Learning—0
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild—0
Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal DistillationCode0
Show:102550
← PrevPage 159 of 413Next →
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