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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9126–9150 of 10307 papers

TitleStatusHype
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks—0
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery—0
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning—0
Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning—0
Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt—0
Transfer learning from language models to image caption generators: Better models may not transfer betterCode0
An introduction to domain adaptation and transfer learning—0
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications—0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching—0
Predicting with Proxies: Transfer Learning in High Dimension—0
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling—0
Low Latency Privacy Preserving InferenceCode0
Motion Blur removal via Coupled Autoencoder—0
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification—0
Temporal Hockey Action Recognition via Pose and Optical Flows—0
An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction—0
Optimizing Quantum Error Correction Codes with Reinforcement Learning—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Deep Metric Transfer for Label Propagation with Limited Annotated DataCode0
Transfer Learning in Astronomy: A New Machine-Learning Paradigm—0
Training on the test set? An analysis of Spampinato et al. [arXiv:1609.00344]—0
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples—0
FDSNet: Finger dorsal image spoof detection network using light field camera—0
Domain Adaptation for Reinforcement Learning on the Atari—0
Deep Transfer Learning for Static Malware ClassificationCode0
Show:102550
← PrevPage 366 of 413Next →
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