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 3176–3200 of 10307 papers

TitleStatusHype
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection—0
Asking and Answering Questions to Extract Event-Argument StructuresCode0
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use CaseCode0
Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification—0
M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition—0
No Train but Gain: Language Arithmetic for training-free Language Adapters enhancementCode0
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks—0
How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching—0
Machine Learning Techniques for MRI Data Processing at Expanding Scale—0
Automated Long Answer Grading with RiceChem DatasetCode0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning—0
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion—0
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning—0
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities—0
Transfer Learning for Molecular Property Predictions from Small Data SetsCode0
Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding—0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
KATO: Knowledge Alignment and Transfer for Transistor Sizing of Different Design and Technology—0
Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models—0
sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model—0
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis—0
Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions—0
GenFighter: A Generative and Evolutive Textual Attack Removal—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