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 9951–9975 of 10307 papers

TitleStatusHype
Transfer Learning for Named-Entity Recognition with Neural Networks—0
WebVision Challenge: Visual Learning and Understanding With Web Data—0
Single Image Action Recognition by Predicting Space-Time Saliency—0
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective—0
A Deep Learning Perspective on the Origin of Facial Expressions—0
Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections—0
Transfer Learning by Ranking for Weakly Supervised Object Annotation—0
Cross-Lingual Parser Selection for Low-Resource Languages—0
Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings—0
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning—0
Deep Feature Learning for Graphs—0
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning—0
Understanding the Mechanisms of Deep Transfer Learning for Medical Images—0
An Interpretable Knowledge Transfer Model for Knowledge Base Completion—0
Morpheo: Traceable Machine Learning on Hidden data—0
Close Yet Distinctive Domain Adaptation—0
Interspecies Knowledge Transfer for Facial Keypoint DetectionCode0
Virtual to Real Reinforcement Learning for Autonomous DrivingCode0
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning—0
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image ClassificationCode0
What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State—0
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised ApproachCode0
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature FusionCode0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Cross-lingual dependency parsing for closely related languages - Helsinki's submission to VarDial 2017—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