跨类别转换的稳定性:稳定性是由不变的神经表示驱动的吗?
Hojin Jang1, Syed Suleman Abbas Zaidi2,3, Xavier Boix4,5
1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, U.S.A. jangh@mit.edu.
Neural computation
|October 16, 2023
概括
深度卷积神经网络 (DCNNs) 在与转换的数据进行训练时,获得了对图像转换的稳定性. 然而,不变的神经表示并不总是驱动这种强度,只有在更多的转换类别中才会出现.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习 机器学习
背景情况:
- 深度卷积神经网络 (DCNNs) 在经过转换数据的训练时表现出对图像转换的稳定性.
- 一个关键假设表明DCNN为这种强度开发了不变的神经表示.
- 另一个解释是为转换和非转换图像提供专门的网络部分.
研究的目的:
- 调查DCNN中不变神经表示的条件.
- 确定不变性是否对于训练数据之外的转换的稳定性至关重要.
- 分析训练数据组成如何影响不变性出现.
主要方法:
- 训练有素的DCNN具有只有特定对象类别被转换的范式.
- 评估了DCNN对类别在培训期间没有被转换的转换的稳定性.
- 分析了基于转换类别的比例的不变表示的出现.
主要成果:
- 不变的神经表示并不始终驱动稳定性;网络在训练类别中显示稳定性而没有不变性.
- 随着培训集中转换类别的数量增加,不变性出现了.
- 在局部转换 (模糊) 中,不变性比在几何转换 (旋转) 中更为突出.
结论:
- 在DCNN中,可以在没有完全不变的情况下实现稳定性.
- 不变的出现取决于培训数据中转换的类别的多样性.
- 了解不变性出现对于开发更强大的深度学习模型至关重要.
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