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

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Showing 2676–2700 of 10307 papers

TitleStatusHype
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR—0
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation—0
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization—0
A multilingual training strategy for low resource Text to Speech—0
Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation—0
Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images—0
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning—0
Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification—0
Foundations of Multivariate Distributional Reinforcement Learning—0
Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities—0
An Empirical Study of Scaling Laws for Transfer—0
Assessing Large Language Models for Online Extremism Research: Identification, Explanation, and New Knowledge—0
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative StudyCode0
On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver—0
CNN Based Detection of Cardiovascular Diseases from ECG Images—0
Data Quality Monitoring through Transfer Learning on Anomaly Detection for the Hadron Calorimeters—0
Adaptive Sample Aggregation In Transfer Learning—0
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction—0
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual LearningCode0
Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis—0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection—0
Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping—0
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language ModelsCode0
GeoTransfer : Generalizable Few-Shot Multi-View Reconstruction via Transfer Learning—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