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 8026–8050 of 10307 papers

TitleStatusHype
Music auto-tagging in the long tail: A few-shot approach—0
PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework—0
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection—0
A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease—0
Mutual Alignment Transfer Learning—0
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks—0
A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis—0
Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer—0
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
Mutual Information-guided Knowledge Transfer for Novel Class Discovery—0
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization—0
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning—0
Mutual Transfer Learning for Massive Data—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
MV2MAE: Multi-View Video Masked Autoencoders—0
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer—0
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation—0
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models—0
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems—0
Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach—0
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches—0
Named Entity Recognition for Novel Types by Transfer Learning—0
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks—0
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data—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