Improving Image Classification Accuracy through Complementary Intra-Class and Inter-Class Mixup

แนวคิดหลัก

Proposing a novel mixup method that enhances intra-class cohesion and integrating it with existing mixup techniques to improve classification accuracy.

บทคัดย่อ

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.

สถิติ

Experimental results demonstrate an average gain of 1.16% using the integrated solution compared to individual methods.

คำพูด

"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."

ข้อมูลเชิงลึกที่สำคัญจาก

Improving Image Classification Accuracy through Complementary Intra-Class and Inter-Class Mixup

by Ye Xu,Ya Gao... ที่ arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14137.pdf

สอบถามเพิ่มเติม

How can the proposed method be adapted for other domains beyond image classification

The proposed method of combining inter-class and intra-class mixup techniques can be adapted for other domains beyond image classification by understanding the underlying principles and adapting them to suit the specific characteristics of those domains. For example, in speech recognition, where MixUp has shown promise, a similar approach could be taken by blending audio representations within the same class (intra-class mixup) to enhance cohesion and then mixing between different classes (inter-class mixup) to improve separability. This comprehensive solution could potentially lead to improved accuracy in speech recognition tasks.

What are potential drawbacks or challenges associated with combining inter-class and intra-class mixup techniques

One potential drawback of combining inter-class and intra-class mixup techniques is the increased complexity in hyperparameter tuning. Balancing the contributions of both types of mixup operations through a hyperparameter like β can be challenging as it requires fine-tuning for optimal performance. Additionally, there may be cases where one type of mixup operation dominates over the other, leading to suboptimal results if not carefully managed. Another challenge could arise from ensuring that synthesized feature representations accurately reflect the true distribution without introducing noise or bias that could impact model performance.

How does randomness in synthesized feature representations impact gradient stochasticity and model performance

The randomness in synthesized feature representations impacts gradient stochasticity and model performance by introducing variability into the training process. By generating random weights for interpolation during intra-class mixup, each mini-batch introduces stochasticity into gradient updates based on these synthesized representations. This randomness helps prevent overfitting by adding diversity to training data and encourages models to generalize better to unseen examples. However, excessive randomness or lack of control over this process can lead to instability during training or hinder convergence if not appropriately managed through careful parameter settings or regularization techniques.