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 5351–5375 of 10307 papers

TitleStatusHype
Robust Internal Representations for Domain Generalization—0
Learning Robust Data Representation: A Knowledge Flow Perspective—0
Robust Learning with Frequency Domain Regularization—0
Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques—0
Robustness and Security Enhancement of Radio Frequency Fingerprint Identification in Time-Varying Channels—0
Robustness of convolutional neural networks to physiological ECG noise—0
Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance—0
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal—0
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning—0
Robust Speech and Natural Language Processing Models for Depression Screening—0
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning—0
Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Robust Transfer Learning with Unreliable Source Data—0
Robust Visual Knowledge Transfer via EDA—0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks—0
Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks—0
Rover: An online Spark SQL tuning service via generalized transfer learning—0
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation—0
RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis—0
RUBERT: A Bilingual Roman Urdu BERT Using Cross Lingual Transfer Learning—0
Rumour Detection via Zero-shot Cross-lingual Transfer Learning—0
Russo-Ukrainian war disinformation detection in suspicious Telegram channels—0
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality—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