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 4451–4500 of 10307 papers

TitleStatusHype
Transfer Learning in _1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis—0
Transfer Learning in General Lensless Imaging through Scattering Media—0
Transfer Learning in Human Activity Recognition: A Survey—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication—0
Transfer Learning in Natural Language Processing—0
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
Transfer Learning for Quantum Classifiers: An Information-Theoretic Generalization Analysis—0
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges—0
Transfer learning in Scalable Graph Neural Network for Improved Physical Simulation—0
Transfer Learning in Spatial-Temporal Forecasting of the Solar Magnetic Field—0
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid—0
Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System—0
Transfer Learning in Visual and Relational Reasoning—0
Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano—0
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets—0
Transfer learning of chaotic systems—0
Transfer learning of feedback head expressions in Danish and Polish comparable multimodal corpora—0
Transfer Learning of fMRI Dynamics—0
Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models—0
Transfer learning of language-independent end-to-end ASR with language model fusion—0
Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification—0
Transfer learning of phase transitions in percolation and directed percolation—0
Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata—0
Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems—0
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations—0
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks—0
Transfer Learning of Tabular Data by Finetuning Large Language Models—0
Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak—0
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond—0
Transfer Learning on Manifolds via Learned Transport Operators—0
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling—0
Transfer Learning on Multi-Fidelity Data—0
Transfer Learning on Transformers for Building Energy Consumption Forecasting -- A Comparative Study—0
Good View Hunting: Learning Photo Composition From Dense View Pairs—0
Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training—0
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning—0
Gradient Sparsification For Masked Fine-Tuning of Transformers—0
Gradient Sparsification For Masked Fine-Tuning of Transformers—0
GradMix: Multi-source Transfer across Domains and Tasks—0
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation—0
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network—0
Grafit: Learning fine-grained image representations with coarse labels—0
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT—0
Grapes disease detection using transfer learning—0
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation—0
Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas—0
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain 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