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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5176–5200 of 10307 papers

TitleStatusHype
Recognition Of Surface Defects On Steel Sheet Using Transfer Learning—0
Recognizing License Plates in Real-Time—0
Recognizing Material Properties from Images—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
Reconnaissance de phones fond\'ee sur du Transfer Learning pour des enfants apprenants lecteurs en environnement de classe (Transfer Learning based phone recognition on children learning to read, with speech recorded in a classroom environment)—0
Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning—0
Reconstructing Training Data From Real World Models Trained with Transfer Learning—0
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems—0
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning—0
Recurrent Neural Network Encoder with Attention for Community Question Answering—0
Recurrent Neural Network for MoonBoard Climbing Route Classification and Generation—0
Recurrent neural networks and transfer learning for elasto-plasticity in woven composites—0
Recurrent Neural Network Training with Dark Knowledge Transfer—0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Recursive Distillation for Open-Set Distributed Robot Localization—0
Recursive Neural Programs: Variational Learning of Image Grammars and Part-Whole Hierarchies—0
Recursive Tree-Structured Self-Attention for Answer Sentence Selection—0
Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering—0
Rediscovering the Alphabet - On the Innate Universal Grammar—0
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis—0
Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models—0
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment—0
Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing—0
RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining—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