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 101–150 of 10307 papers

TitleStatusHype
Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better—0
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data—0
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective—0
When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?Code0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
Transfer learning for multifidelity simulation-based inference in cosmology—0
A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks—0
Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off—0
Intelligent Incident Hypertension Prediction in Obstructive Sleep Apnea—0
A domain adaptation neural network for digital twin-supported fault diagnosisCode0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data—0
Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks—0
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning DistillationCode0
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation ModelsCode0
ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers—0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning—0
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain RecommendationCode0
A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking—0
Making deep neural networks work for medical audio: representation, compression and domain adaptation—0
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning—0
Neural Parameter Search for Slimmer Fine-Tuned Models and Better TransferCode0
Knowledge Grafting of Large Language ModelsCode0
Wasserstein Transfer Learning—0
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