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 4501–4550 of 10307 papers

TitleStatusHype
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Multi-Task Supervised Pretraining for Neural Domain Adaptation—0
Multi-task transfer learning for finding actionable information from crisis-related messages on social media—0
Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response—0
Multivariate and Online Transfer Learning with Uncertainty Quantification—0
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision—0
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data—0
Multi-way VNMT for UGC: Improving Robustness and Capacity via Mixture Density Networks—0
MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid—0
muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems—0
Music auto-tagging in the long tail: A few-shot approach—0
Mutual Alignment Transfer Learning—0
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks—0
Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer—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
MV2MAE: Multi-View Video Masked Autoencoders—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
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish—0
Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review—0
Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis—0
Natural Language Robot Programming: NLP integrated with autonomous robotic grasping—0
Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT—0
Naver Labs Europe's Systems for the Document-Level Generation and Translation Task at WNGT 2019—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning—0
Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment—0
NCART: Neural Classification and Regression Tree for Tabular Data—0
Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection—0
Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach—0
Near-Optimal Linear Regression under Distribution Shift—0
Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks—0
Negation Detection in Dutch Spoken Human-Computer Conversations—0
NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil—0
Neonatal Face and Facial Landmark Detection from Video Recordings—0
Neonatal Pain Expression Recognition Using Transfer Learning—0
Healthcare NER Models Using Language Model Pretraining—0
Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe—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