Kernekoncepter
Proposing a novel mixup method that enhances intra-class cohesion and integrating it with existing mixup techniques to improve classification accuracy.
Resumé
The content discusses the limitations of current mixup methods in image classification tasks, introduces a novel mixup approach focusing on intra-class mixup, and presents an integrated solution combining inter-class and intra-class mixup. Experimental results demonstrate significant improvements in classification accuracy across various datasets.
Abstract:
MixUp and its variants have limitations in image classification tasks.
Proposed novel mixup method targets intra-class mixup for enhanced cohesion.
Integrated solution combines inter- and intra-class mixup for improved accuracy.
Introduction:
Data augmentation techniques like MixUp aim to enhance model performance.
Current methods neglect intra-class mixing, limiting classification performance.
Proposed method strengthens intra-class cohesion through targeted mixing operations.
Methodology:
Supplementation Component ensures each class has at least two images in a mini-batch.
Intra-Class Mixup Component generates synthesized feature representations within the same class.
Inter-Class Mixup Component blends images or hidden representations between different classes.
Integration Component combines losses from both components with a balancing hyperparameter.
Results:
Experimental setup includes diverse datasets, model input sizes, architectures, and pre-trained models.
Comparative analysis of five methods shows the effectiveness of integrating inter-class and intra-class mixup techniques.
Performance evaluation demonstrates improved classification accuracy with the proposed integrated solution.
Statistik
Experimental results demonstrate an average gain of 1.16% using the integrated solution compared to individual methods.
Citater
"Our integrated solution achieves a 0.1% to 3.43% higher accuracy than the best of either MixUp or our intra-class mixup method."
"Experimental results conclusively validate the effectiveness of our integrated solution in improving classification accuracy."