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 3576–3600 of 10307 papers

TitleStatusHype
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
A scoping review of transfer learning research on medical image analysis using ImageNet—0
Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification—0
A Scenario-Based Functional Testing Approach to Improving DNN Performance—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification—0
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs—0
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—0
Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth—0
CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images—0
Command-line Risk Classification using Transformer-based Neural Architectures—0
Come hither or go away? Recognising pre-electoral coalition signals in the news—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends—0
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems—0
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information—0
Show:102550
← PrevPage 144 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