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 7826–7850 of 10307 papers

TitleStatusHype
Terahertz Pulse Shaping Using Diffractive Surfaces—0
Model-based Reinforcement Learning: A Survey—0
Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices—0
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
A shared neural encoding model for the prediction of subject-specific fMRI responseCode0
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People—0
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities—0
Robustifying Sequential Neural Processes—0
Improving neural network predictions of material properties with limited data using transfer learning—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
Ensemble Transfer Learning for Emergency Landing Field Identification on Moderate Resource Heterogeneous Kubernetes Cluster—0
Transfer Learning via _1 Regularization—0
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification—0
Backdoor Attacks Against Deep Learning Systems in the Physical World—0
SOAC: The Soft Option Actor-Critic Architecture—0
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM—0
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction—0
X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data—0
Meta Transfer Learning for Emotion Recognition—0
DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery—0
Supervised Understanding of Word Embeddings—0
Limits of Transfer Learning—0
A General Class of Transfer Learning Regression without Implementation Cost—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