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 2401–2425 of 10307 papers

TitleStatusHype
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publicationsCode0
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs—0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios—0
MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics ClassificationCode0
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module—0
Zero-shot Cross-lingual Transfer Learning with Multiple Source and Target Languages for Information Extraction: Language Selection and Adversarial Training—0
Transfer Learning Guided Noise Reduction for Automatic Modulation Classification—0
Federated Graph Learning with Graphless Clients—0
Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection—0
Quantifying Knowledge Distillation Using Partial Information Decomposition—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning—0
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior PredictionCode0
A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning—0
Deep Nonparametric Conditional Independence Tests for ImagesCode0
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction—0
Towards Equitable ASD Diagnostics: A Comparative Study of Machine and Deep Learning Models Using Behavioral and Facial Data—0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive Outcome for Infant Brain Injury—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
Fine-tuning -- a Transfer Learning approach—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