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 8601–8625 of 10307 papers

TitleStatusHype
Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series—0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
Performance of Transfer Learning Model vs. Traditional Neural Network in Low System Resource Environment—0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
Per-pixel Classification Rebar Exposures in Bridge Eye-inspection—0
PersianMind: A Cross-Lingual Persian-English Large Language Model—0
Persian Natural Language Inference: A Meta-learning approach—0
The impact of near domain transfer on biomedical named entity recognition—0
Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models—0
Successor Feature Neural Episodic Control—0
A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability—0
Personalization of Wearable Sensor-Based Joint Kinematic Estimation Using Computer Vision for Hip Exoskeleton Applications—0
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization—0
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge—0
Personalized Dynamics Models for Adaptive Assistive Navigation Systems—0
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer—0
Personalized Federated Learning via Backbone Self-Distillation—0
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer—0
Personalized Human Activity Recognition Using Convolutional Neural Networks—0
Personalized Patent Claim Generation and Measurement—0
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts—0
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes—0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections—0
Personalizing a Dialogue System with Transfer Reinforcement Learning—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