衡量和表示学习方法的评估:由相对距离驱动的表示对绩效的影响
Anthony B Garza1, Rolando Garcia1, Marc S Halfon2
1Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX, USA.
概括
三重网络架构是最有效的通过学习相对距离来测量图像相似性. 将其与自动编码器相结合是不必要的,但可分离的卷积层提供了一个可行的替代方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络越来越多地用于度量学习.
- 对图像相似性的不同架构的评估至关重要.
研究的目的:
- 确定最有效的深度神经网络架构用于图像相似性和不相似性测量.
- 为了比较变量自动编码器,语网络,三联网络,以及它们的组合与基线卷积网络.
主要方法:
- 在标准图像数据集上评估了六个深度神经网络架构.
- 包括变化自编码器,语网络,三重网络,组合架构和基线可分离的卷积网络.
主要成果:
- 由于学习相对距离,三重网络表现出卓越的性能.
- 将自动编码器与度量学习网络相结合,没有显著的好处.
- 可分离的卷积层架构提供了一个实用和有效的替代方案.
结论:
- 三重网络是最佳的图像度量学习,通过专注于相对距离.
- 不建议将自动编码器集成到度量学习架构中.
- 可分离的卷积网络为图像相似性任务提供了一个有希望的,更简单的替代方案.
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