对比传统CNN与注意力增强CNN在图像分类中的表现
CNNtention: Can CNNs do better with Attention?
- 在标准CNN中引入注意力机制提升全局上下文感知
- 注意力增强模型准确率更高，但计算开销更大
- 适合需要高精度且资源充足的视觉任务
卷积神经网络（CNN）长期作为图像分类的标准架构，而基于注意力的机制近年来日益受到关注。本项目旨在通过图像分类任务，比较传统CNN与注意力增强型CNN的性能、准确率及计算效率。研究揭示了传统CNN局部特征提取的优势与注意力增强型CNN捕捉全局上下文的潜力，同时分析了两者的权衡。结果有助于根据具体应用场景选择合适模型，并深化对深度学习架构的理解。本工作为佐治亚理工学院CS7643深度学习课程的期末项目。
原文摘要 · Abstract (English)
Convolutional Neural Networks (CNNs) have been the standard for image classification tasks for a long time, but more recently attention-based mechanisms have gained traction. This project aims to compare traditional CNNs with attention-augmented CNNs across an image classification task. By evaluating and comparing their performance, accuracy and computational efficiency, the project will highlight benefits and trade-off of the localized feature extraction of traditional CNNs and the global context capture in attention-augmented CNNs. By doing this, we can reveal further insights into their respective strengths and weaknesses, guide the selection of models based on specific application needs and ultimately, enhance understanding of these architectures in the deep learning community. This was our final project for CS7643 Deep Learning course at Georgia Tech.
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