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 6076–6100 of 10307 papers

TitleStatusHype
General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance—0
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries—0
Generalisation in Lifelong Reinforcement Learning through Logical Composition—0
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with Cone-Beam Computed Tomography (CBCT) to Computed Tomography (CT) image conversion—0
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors—0
Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning—0
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images—0
A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning—0
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy—0
Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers—0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles—0
Generalization error of min-norm interpolators in transfer learning—0
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers—0
Generalization Guarantees for Neural Architecture Search with Train-Validation Split—0
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics—0
Generalization in data-driven models of primary visual cortex—0
Generalization in medical AI: a perspective on developing scalable models—0
Generalization in Neural Networks: A Broad Survey—0
Generalization in Transfer Learning—0
Generalization of feature embeddings transferred from different video anomaly detection domains—0
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes—0
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment—0
Class-imbalanced Domain Adaptation: An Empirical Odyssey—0
Show:102550
← PrevPage 244 of 413Next →
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