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 8601–8650 of 10307 papers

TitleStatusHype
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs—0
Experience Reuse with Probabilistic Movement Primitives—0
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task DistillationCode0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing—0
Fine-Tuning Models Comparisons on Garbage Classification for Recyclability—0
Progressive Transfer LearningCode0
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval—0
Relative Afferent Pupillary Defect Screening through Transfer Learning—0
Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning—0
Distilling Knowledge From a Deep Pose Regressor Network—0
MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter—0
Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task—0
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task—0
CUNI Submission for Low-Resource Languages in WMT News 2019—0
The University of Maryland's Kazakh-English Neural Machine Translation System at WMT19—0
The RWTH Aachen University Machine Translation Systems for WMT 2019—0
The Universitat d'Alacant Submissions to the English-to-Kazakh News Translation Task at WMT 2019—0
Biologically inspired sleep algorithm for artificial neural networks—0
Cross-domain Network Representations—0
A Survey on Deep Learning of Small Sample in Biomedical Image AnalysisCode0
Robust Deep Sensing Through Transfer Learning in Cognitive Radio—0
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions—0
hULMonA: The Universal Language Model in ArabicCode0
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation—0
MSIT\_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.—0
Improved Generalization of Arabic Text Classifiers—0
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts—0
Transfer Learning for Health-related Twitter DataCode0
Transfer Learning from Pre-trained BERT for Pronoun Resolution—0
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention—0
Interior Object Detection and Color Harmonization—0
Few-Shot Meta-Denoising—0
Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation—0
Towards More Accurate Automatic Sleep Staging via Deep Transfer LearningCode0
Zero-shot transfer for implicit discourse relation classificationCode0
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets—0
KNEEL: Knee Anatomical Landmark Localization Using Hourglass NetworksCode1
Learnable Parameter SimilarityCode0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images—0
Semisupervised Adversarial Neural Networks for Cyber Security Transfer Learning—0
Knowledge transfer in deep block-modular neural networks—0
Gait recognition via deep learning of the center-of-pressure trajectory—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