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 3101–3125 of 10307 papers

TitleStatusHype
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs—0
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning—0
Modeling of Time-varying Wireless Communication Channel with Fading and ShadowingCode0
Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer—0
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor—0
Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer—0
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
Scalable Learning of Segment-Level Traffic Congestion Functions—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction—0
Model Inversion Robustness: Can Transfer Learning Help?—0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
Large Language Model Enhanced Machine Learning Estimators for ClassificationCode0
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—0
Hypergraph-enhanced Dual Semi-supervised Graph Classification—0
Large Language Models for Cyber Security: A Systematic Literature Review—0
Deep Learning Method to Predict Wound Healing Progress Based on Collagen Fibers in Wound Tissue—0
Deep learning-based variational autoencoder for classification of quantum and classical states of light—0
Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches—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