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 8476–8500 of 10307 papers

TitleStatusHype
Manifold Embedded Knowledge Transfer for Brain-Computer InterfacesCode0
Actor Critic with Differentially Private Critic—0
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image DenoisingCode0
Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer LearningCode0
How to Not Measure Disentanglement—0
Deep Transfer Learning for Source Code ModelingCode0
Model Fusion via Optimal TransportCode0
Aff-Wild Database and AffWildNetCode0
Automatic segmentation of texts into units of meaning for reading assistance—0
Multi-modal Deep Analysis for Multimedia—0
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor—0
DDTCDR: Deep Dual Transfer Cross Domain Recommendation—0
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge—0
Conversational Transfer Learning for Emotion Recognition—0
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified FrameworkCode0
Learning protein conformational space by enforcing physics with convolutions and latent interpolations—0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
ATL: Autonomous Knowledge Transfer from Many Streaming ProcessesCode0
Semi Few-Shot Attribute Translation—0
Linking emotions to behaviors through deep transfer learningCode0
One-To-Many Multilingual End-to-end Speech Translation—0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Graph Few-shot Learning via Knowledge TransferCode0
Semantic Preserving Generative Adversarial Models—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