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 1651–1675 of 10307 papers

TitleStatusHype
Scalable Graph Generative Modeling via Substructure SequencesCode0
Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift—0
Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis—0
WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection—0
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation—0
An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI—0
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference—0
On the Generalization vs Fidelity Paradox in Knowledge DistillationCode0
GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer—0
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy—0
Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI—0
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation—0
Transfer of Structural Knowledge from Synthetic LanguagesCode0
Transfer Learning from Visual Speech Recognition to Mouthing Recognition in German Sign LanguageCode0
LOD1 3D City Model from LiDAR: The Impact of Segmentation Accuracy on Quality of Urban 3D Modeling and Morphology ExtractionCode0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Dual Decomposition of Weights and Singular Value Low Rank Adaptation—0
DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models—0
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data—0
MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation TasksCode0
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning—0
Domain Adaptation of VLM for Soccer Video Understanding—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