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 9901–9925 of 10307 papers

TitleStatusHype
Distral: Robust Multitask Reinforcement Learning—0
Deep Learning-Based Communication Over the Air—0
Improving speaker turn embedding by crossmodal transfer learning from face embedding—0
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition—0
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext TaskCode0
The Complex Negotiation Dialogue Game—0
Multilingual Hierarchical Attention Networks for Document ClassificationCode0
Zero-Shot Transfer Learning for Event ExtractionCode0
Discriminatory Transfer—0
Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning—0
Hidden Layers in Perceptual Learning—0
Attentional Push: A Deep Convolutional Network for Augmenting Image Salience With Shared Attention Modeling in Social Scenes—0
A Gift From Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning—0
MuCaLe-Net: Multi Categorical-Level Networks to Generate More Discriminating Features—0
Learning bilingual word embeddings with (almost) no bilingual data—0
Active Sentiment Domain Adaptation—0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally—0
AGA: Attribute-Guided AugmentationCode0
SMC Faster R-CNN: Toward a scene-specialized multi-object detector—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR—0
Two-Stage Synthesis Networks for Transfer Learning in Machine ComprehensionCode0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
Object Detection Using Deep CNNs Trained on Synthetic Images—0
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision ProcessesCode0
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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