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 8251–8275 of 10307 papers

TitleStatusHype
A Common Semantic Space for Monolingual and Cross-Lingual Meta-EmbeddingsCode0
Driver Safety Development Real Time Driver Drowsiness Detection System Based on Convolutional Neural Network—0
Model-Driven Beamforming Neural Networks—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
Towards detection and classification of microscopic foraminifera using transfer learningCode0
Boosting Deep Face Recognition via Disentangling Appearance and Geometry—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data—0
Multi-Scale Weight Sharing Network for Image Recognition—0
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers—0
Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning—0
SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature LearningCode0
Multipurpose Intelligent Process Automation via Conversational Assistant—0
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition—0
Generalizing Emergent Communication—0
Exploring Benefits of Transfer Learning in Neural Machine Translation—0
COPD Classification in CT Images Using a 3D Convolutional Neural Network—0
A Comprehensive Survey of Multilingual Neural Machine Translation—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization—0
On the comparability of Pre-trained Language Models—0
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition—0
Meta Variance Transfer: Learning to Augment from the Others—0
Mutual Transfer Learning for Massive Data—0
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices—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