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 4476–4500 of 10307 papers

TitleStatusHype
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