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 6551–6600 of 10307 papers

TitleStatusHype
Region Semantically Aligned Network for Zero-Shot Learning—0
Regret Bounds for Lifelong Learning—0
Regularization techniques for fine-tuning in neural machine translation—0
Regularization Through Simultaneous Learning: A Case Study on Plant Classification—0
Compositional Transfer in Hierarchical Reinforcement Learning—0
Regularized Multi-output Gaussian Convolution Process with Domain Adaptation—0
Regularized Soft Actor-Critic for Behavior Transfer Learning—0
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks—0
Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning—0
Towards interpretable quantum machine learning via single-photon quantum walks—0
Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks—0
Reinforcement Learning by Guided Safe Exploration—0
Reinforcement Learning for Systematic FX Trading—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
ReINTEL Challenge 2020: Exploiting Transfer Learning Models for Reliable Intelligence Identification on Vietnamese Social Network Sites—0
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks—0
Relation-Aware Graph Foundation Model—0
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection—0
Relative Afferent Pupillary Defect Screening through Transfer Learning—0
Relative Density-Ratio Estimation for Robust Distribution Comparison—0
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings—0
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs—0
Reliability and Robustness of Transformers for Automated Short-Answer Grading—0
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery—0
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion—0
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization—0
Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models—0
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection—0
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network—0
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques—0
Remote Sensing Image Enhancement through Spatiotemporal Filtering—0
Removing Rain Streaks via Task Transfer Learning—0
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction—0
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning—0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning—0
Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition—0
Rep-Net: Efficient On-Device Learning via Feature Reprogramming—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Representational Distance Learning for Deep Neural Networks—0
Representational Transfer Learning for Matrix Completion—0
Representation learning from videos in-the-wild: An object-centric approach—0
Representation Purification for End-to-End Speech Translation—0
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery—0
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning—0
Representation Topology Divergence: A Method for Comparing Neural Network Representations.—0
Representation Transfer by Optimal Transport—0
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression—0
Re-presenting a Story by Emotional Factors using Sentimental Analysis Method—0
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model—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