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 1601–1625 of 10307 papers

TitleStatusHype
QMamba: On First Exploration of Vision Mamba for Image Quality AssessmentCode0
WMAdapter: Adding WaterMark Control to Latent Diffusion Models—0
PRIBOOT: A New Data-Driven Expert for Improved Driving SimulationsCode0
Differentially Private Prototypes for Imbalanced Transfer Learning—0
Measuring training variability from stochastic optimization using robust nonparametric testing—0
Strategies for Pretraining Neural OperatorsCode0
Indoor Fire and Smoke Detection Using Soft-Voting Based Deep Ensemble ModelCode0
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning—0
Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification—0
Transferring Knowledge from Large Foundation Models to Small Downstream Models—0
BertaQA: How Much Do Language Models Know About Local Culture?Code0
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection—0
Large Language Models are Limited in Out-of-Context Knowledge ReasoningCode0
Augmenting Offline RL with Unlabeled Data—0
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive LearningCode0
SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection—0
Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy—0
Contrastive learning of T cell receptor representationsCode0
A Statistical Theory of Regularization-Based Continual Learning—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Utilizing Grounded SAM for self-supervised frugal camouflaged human detection—0
Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation—0
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models—0
DeviceBERT: Applied Transfer Learning With Targeted Annotations and Vocabulary Enrichment to Identify Medical Device and Component Terminology in FDA Recall Summaries—0
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-TrainingCode1
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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