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 9776–9800 of 10307 papers

TitleStatusHype
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
Overcoming data scarcity with transfer learning—0
End-to-End Deep Learning of Optimization HeuristicsCode0
Data, Depth, and Design: Learning Reliable Models for Skin Lesion AnalysisCode0
Generating Stylistically Consistent Dialog Responses with Transfer Learning—0
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation—0
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
Interpretable Deep Learning applied to Plant Stress Phenotyping—0
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation—0
Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning—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