论文标题
卷积神经网络编码多少位置信息？
How Much Position Information Do Convolutional Neural Networks Encode?
论文作者
论文摘要
与完全连接的网络相反，卷积神经网络（CNN）通过学习与本地过滤器相关的有限空间范围来实现效率。这意味着过滤器可能知道它在看什么，而不是将其放在图像中的位置。有关绝对位置的信息本质上是有用的，并且可以合理地假设，如果有一种方法，则深入CNN可以隐含地学习编码此信息。在本文中，我们检验了这一假设，揭示了在常用神经网络中编码的绝对位置信息的惊人程度。一组全面的实验表明，该假设的有效性，并阐明了该信息的表示以及在向深度CNN中派生的位置信息提供的线索时所代表的何处。
In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. Information concerning absolute position is inherently useful, and it is reasonable to assume that deep CNNs may implicitly learn to encode this information if there is a means to do so. In this paper, we test this hypothesis revealing the surprising degree of absolute position information that is encoded in commonly used neural networks. A comprehensive set of experiments show the validity of this hypothesis and shed light on how and where this information is represented while offering clues to where positional information is derived from in deep CNNs.
