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 8551–8575 of 10307 papers

TitleStatusHype
Transfer Alignment Network for Double Blind Unsupervised Domain Adaptation—0
Efficiently Reusing Old Models Across Languages via Transfer Learning—0
Pretraining boosts out-of-domain robustness for pose estimation—0
How to improve CNN-based 6-DoF camera pose estimation—0
Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and "transfer learning" technique—0
Model-Based and Data-Driven Strategies in Medical Image Computing—0
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer LearningCode0
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles—0
Cross-lingual Dependency Parsing with Unlabeled Auxiliary LanguagesCode0
Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom—0
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages—0
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI—0
Transfer Learning using CNN for Handwritten Devanagari Character Recognition—0
Timage -- A Robust Time Series Classification PipelineCode0
Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition—0
Transfer Learning with Dynamic Adversarial Adaptation Network—0
Text Length Adaptation in Sentiment ClassificationCode0
Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer—0
Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer LearningCode0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
Cycle-consistent Conditional Adversarial Transfer NetworksCode0
Transfer Learning with Dynamic Distribution Adaptation—0
Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models—0
MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech—0
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model—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