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 9751–9800 of 10307 papers

TitleStatusHype
Transfer Learning in CNNs Using Filter-Trees—0
Smartphone App Usage Prediction Using Points of Interest—0
Expectation maximization transfer learning and its application for bionic hand prostheses—0
Adversarial Transfer Learning for Cross-domain Visual Recognition—0
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic ImageryCode0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce—0
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video ClassificationCode1
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity RecognitionCode0
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches—0
Domain Generalization by Marginal Transfer LearningCode0
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application—0
Zero-shot Learning via Shared-Reconstruction-Graph Pursuit—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
Vision Based Railway Track Monitoring using Deep Learning—0
Improvements to context based self-supervised learning—0
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception—0
Zero-Annotation Object Detection with Web Knowledge Transfer—0
Knowledge transfer for surgical activity prediction—0
Supervised and Unsupervised Transfer Learning for Question Answering—0
All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification—0
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems—0
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension—0
Self-Supervised Intrinsic Image Decomposition—0
Multi-Relevance Transfer Learning—0
Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining—0
Distribution-Preserving k-Anonymity—0
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning—0
Convolutional Drift Networks for Video Classification—0
End-to-End Deep Learning of Optimization HeuristicsCode0
Overcoming data scarcity with transfer learning—0
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation—0
Generating Stylistically Consistent Dialog Responses with Transfer Learning—0
Data, Depth, and Design: Learning Reliable Models for Skin Lesion AnalysisCode0
Common Representation Learning Using Step-based Correlation Multi-Modal CNNCode0
Transfer Learning to Learn with Multitask Neural Model Search—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
Deep Transfer Learning for Error Decoding from Non-Invasive EEG—0
Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning—0
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation—0
Interpretable Deep Learning applied to Plant Stress Phenotyping—0
Listening to the World Improves Speech Command Recognition—0
Neural Stain-Style Transfer Learning using GAN for Histopathological ImagesCode0
Distributed Deep Transfer Learning by Basic Probability Assignment—0
Historical Document Image Segmentation with LDA-Initialized Deep Neural NetworksCode0
Material Classification using Neural Networks—0
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation—0
Decision support from financial disclosures with deep neural networks and transfer learningCode0
Neural Program Meta-Induction—0
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks—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