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 9801–9850 of 10307 papers

TitleStatusHype
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Historical Document Image Segmentation with LDA-Initialized Deep Neural NetworksCode0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
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks—0
Neural Program Meta-Induction—0
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer—0
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks—0
MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language ProcessingCode0
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring—0
SubUNets: End-To-End Hand Shape and Continuous Sign Language RecognitionCode0
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories—0
Predictor Combination at Test Time—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
A Study of Convolutional Architectures for Handshape Recognition applied to Sign LanguageCode0
Are we done with object recognition? The iCub robot's perspectiveCode0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
Constrained Deep Transfer Feature Learning and its Applications—0
Attention-based Wav2Text with Feature Transfer Learning—0
Structured Probabilistic Pruning for Convolutional Neural Network AccelerationCode0
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement LearningCode0
Estimated Depth Map Helps Image ClassificationCode0
Transfer learning from synthetic to real images using variational autoencoders for robotic applications—0
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
Object Recognition from very few Training Examples for Enhancing Bicycle Maps—0
Long-Term Ensemble Learning of Visual Place Classifiers—0
Shapechanger: Environments for Transfer LearningCode0
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding—0
Unsupervised state representation learning with robotic priors: a robustness benchmark—0
Viewpoint Invariant Action Recognition using RGB-D Videos—0
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders—0
Shared Learning : Enhancing Reinforcement in Q-Ensembles—0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination ChangeCode0
Optimal Transport for Deep Joint Transfer Learning—0
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification—0
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition—0
Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory AnalysisCode0
Group-level Emotion Recognition using Transfer Learning from Face IdentificationCode0
The Devil is in the Tails: Fine-grained Classification in the Wild—0
Knowledge Transfer Between Artificial Intelligence Systems—0
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models—0
``Deep'' Learning : Detecting Metaphoricity in Adjective-Noun Pairs—0
Cross-lingual Character-Level Neural Morphological Tagging—0
NITE: A Neural Inductive Teaching Framework for Domain Specific NER—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community—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