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 5501–5525 of 10307 papers

TitleStatusHype
The Missing Link: Finding label relations across datasets—0
Data-Efficient Double-Win Lottery Tickets from Robust Pre-trainingCode0
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning—0
Neural Collapse: A Review on Modelling Principles and Generalization—0
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models—0
Discrete State-Action Abstraction via the Successor RepresentationCode0
EiX-GNN : Concept-level eigencentrality explainer for graph neural networksCode0
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flowCode0
Transfer learning to decode brain states reflecting the relationship between cognitive tasks—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
Transfer Learning based Search Space Design for Hyperparameter Tuning—0
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning—0
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection—0
Evaluation-oriented Knowledge Distillation for Deep Face Recognition—0
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization GuaranteesCode0
MorisienMT: A Dataset for Mauritian Creole Machine Translation—0
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationsCode0
MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling—0
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical ImagingCode0
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation—0
Examining the behaviour of state-of-the-art convolutional neural networks for brain tumor detection with and without transfer learning—0
Enriching a Fashion Knowledge Graph from Product Textual Descriptions—0
Learning Unbiased Transferability for Domain Adaptation by Uncertainty ModelingCode0
Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection—0
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets—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