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 126–150 of 10307 papers

TitleStatusHype
X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI—0
Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control—0
Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift—0
Reward-Aware Proto-Representations in Reinforcement Learning—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—0
Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
End-to-End Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries—0
Scalable Graph Generative Modeling via Substructure SequencesCode0
WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation—0
An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI—0
An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection—0
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy—0
On the Generalization vs Fidelity Paradox in Knowledge DistillationCode0
Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI—0
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation—0
Transfer of Structural Knowledge from Synthetic LanguagesCode0
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer—0
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation TasksCode0
LOD1 3D City Model from LiDAR: The Impact of Segmentation Accuracy on Quality of Urban 3D Modeling and Morphology ExtractionCode0
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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