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 7651–7675 of 10307 papers

TitleStatusHype
A Generative Adversarial Approach To ECG Synthesis And DenoisingCode0
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning—0
A general approach to bridge the reality-gap—0
A New Multiple Source Domain Adaptation Fault Diagnosis Method between Different Rotating Machines—0
Large Dimensional Analysis and Improvement of Multi Task Learning—0
Transfer learning for nonlinear dynamics and its application to fluid turbulence—0
Problems in AI research and how the SP System may help to solve them—0
Breast mass detection in digital mammography based on anchor-free architecture—0
Cross-lingual Transfer Learning for Semantic Role Labeling in Russian—0
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation—0
Unsupervised Domain Adaptation with Progressive Adaptation of SubspacesCode0
Evaluating Knowledge Transfer in Neural Network for Medical Images—0
Knowledge Efficient Deep Learning for Natural Language Processing—0
A transfer learning metamodel using artificial neural networks applied to natural convection flows in enclosuresCode0
On Transfer Learning of Traditional Frequency and Time Domain Features in Turning—0
All About Knowledge Graphs for Actions—0
Few-Shot Object Detection via Knowledge Transfer—0
Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation—0
A Flexible Selection Scheme for Minimum-Effort Transfer Learning—0
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction—0
Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes—0
An End-to-End Attack on Text-based CAPTCHAs Based on Cycle-Consistent Generative Adversarial Network—0
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer LearningCode0
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach—0
3D for Free: Crossmodal Transfer Learning using HD Maps—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