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 2901–2925 of 10307 papers

TitleStatusHype
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models—0
Multi-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching—0
Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints—0
Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation—0
Malaria Cell Detection Using Deep Neural Networks—0
AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image SegmentationCode0
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning TechniquesCode0
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques: A Comparative AnalysisCode0
VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
Learn it or Leave it: Module Composition and Pruning for Continual Learning—0
LABOR-LLM: Language-Based Occupational Representations with Large Language Models—0
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning—0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Leveraging Parameter-Efficient Transfer Learning for Multi-Lingual Text-to-Speech Adaptation—0
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting—0
WAVE: Weight Template for Adaptive Initialization of Variable-sized Models—0
Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance—0
When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization—0
Federated Transfer Learning Aided Interference Classification in GNSS Signals—0
Bone Fracture Classification using Transfer LearningCode0
Multi-Domain Evolutionary Optimization of Network Structures—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