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 3701–3725 of 10307 papers

TitleStatusHype
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review—0
How much data do I need? A case study on medical data—0
Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification—0
Untargeted Code Authorship Evasion with Seq2Seq Transformation—0
One-Shot Transfer Learning for Nonlinear ODEs—0
nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla Sentiment Analysis—0
A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN—0
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation—0
Machine Translation for Ge'ez Language—0
Data-driven Prior Learning for Bayesian OptimisationCode0
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study—0
Video Anomaly Detection using GAN—0
Knowledge Distillation Based Semantic Communications For Multiple Users—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition—0
Recurrent neural networks and transfer learning for elasto-plasticity in woven composites—0
Transfer Learning-based Real-time Handgun Detection—0
EA-KD: Entropy-based Adaptive Knowledge Distillation—0
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer—0
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry—0
Lung cancer detection from thoracic CT scans using an ensemble of deep learning modelsCode0
InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions—0
Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images—0
Event Camera Data Dense Pre-training—0
Open Set Dandelion Network for IoT Intrusion Detection—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