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 3901–3925 of 10307 papers

TitleStatusHype
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object ClassificationCode0
Tight Rates in Supervised Outlier Transfer Learning—0
Enhancing the Authenticity of Rendered Portraits with Identity-Consistent Transfer Learning—0
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations—0
Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection—0
Amortized Network Intervention to Steer the Excitatory Point Processes—0
Robust Transfer Learning with Unreliable Source Data—0
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights—0
Assessing Electricity Service Unfairness with Transfer Counterfactual Learning—0
T-GAE: Transferable Graph Autoencoder for Network AlignmentCode0
Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning—0
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication—0
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches—0
Learning to Prompt Your Domain for Vision-Language Models—0
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer—0
Crossed-IoT device portability of Electromagnetic Side Channel Analysis: Challenges and Dataset—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs—0
Graph Neural Network-based EEG Classification: A Survey—0
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment—0
An evaluation of pre-trained models for feature extraction in image classification—0
MarineDet: Towards Open-Marine Object Detection—0
PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics—0
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to ScaleCode0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
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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