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 1751–1775 of 10307 papers

TitleStatusHype
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning—0
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imageryCode1
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning—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
Modeling of Time-varying Wireless Communication Channel with Fading and ShadowingCode0
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor—0
Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer—0
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer—0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CTCode2
Novel Class Discovery for Ultra-Fine-Grained Visual CategorizationCode1
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual GroundingCode1
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction—0
Scalable Learning of Segment-Level Traffic Congestion Functions—0
Model Inversion Robustness: Can Transfer Learning Help?—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
Deep learning-based variational autoencoder for classification of quantum and classical states of light—0
Deep Learning Method to Predict Wound Healing Progress Based on Collagen Fibers in Wound Tissue—0
Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—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