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 5776–5800 of 10307 papers

TitleStatusHype
Gender bias Evaluation in Luganda-English Machine Translation—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning—0
Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production—0
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
A Study on Using Different Audio Lengths in Transfer Learning for Improving Chainsaw Sound Recognition—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
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection—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
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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