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 3701–3725 of 10307 papers

TitleStatusHype
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning—0
Label-efficient Time Series Representation Learning: A Review—0
LiT Tuned Models for Efficient Species DetectionCode0
Transfer Learning for Bayesian Optimization: A Survey—0
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain RecommendationCode0
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks—0
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals—0
Robust Knowledge Transfer in Tiered Reinforcement LearningCode0
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime—0
Text recognition on images using pre-trained CNN—0
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features—0
Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge TransferCode0
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural NetworksCode1
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domainsCode1
Offsite-Tuning: Transfer Learning without Full ModelCode2
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size ScheduleCode1
Neonatal Face and Facial Landmark Detection from Video Recordings—0
Rover: An online Spark SQL tuning service via generalized transfer learning—0
Investigating the role of model-based learning in exploration and transfer—0
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image SegmentationCode1
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition—0
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals—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