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 4951–4975 of 10307 papers

TitleStatusHype
Compiler Provenance Recovery for Multi-CPU Architectures Using a Centrifuge Mechanism—0
An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning—0
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer—0
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks—0
Transfer Learning for Contextual Multi-armed Bandits—0
Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning—0
Classification of Melanocytic Nevus Images using BigTransfer (BiT)—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models—0
Towards continually learning new languages—0
Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms—0
Orientation recognition and correction of Cardiac MRI with deep neural networkCode0
Novel transfer learning schemes based on Siamese networks and synthetic dataCode0
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization—0
Federated deep transfer learning for EEG decoding using multiple BCI tasks—0
A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi—0
Frozen Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks—0
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors—0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Impact of visual assistance for automated audio captioning—0
Targeted Attention for Generalized- and Zero-Shot Learning—0
CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts—0
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine—0
Brain informed transfer learning for categorizing construction hazards—0
Transfer learning for tensor Gaussian graphical models—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