Foveated vision, a trait shared by many animals, including humans, has not
been fully utilized in machine learning applications, despite its significant
contributions to biological visual function. This study investigates whether
retinotopic mapping, a critical component of foveated vision, can enhance image
categorization and localization performance when integrated into deep
convolutional neural networks (CNNs). Retinotopic mapping was integrated into
the inputs of standard off-the-shelf convolutional neural networks (CNNs),
which were then retrained on the ImageNet task. As expected, the
logarithmic-polar mapping improved the network's ability to handle arbitrary
image zooms and rotations, particularly for isolated objects. Surprisingly, the
retinotopically mapped network achieved comparable performance in
classification. Furthermore, the network demonstrated improved classification
localization when the foveated center of the transform was shifted. This
replicates a crucial ability of the human visual system that is absent in
typical convolutional neural networks (CNNs). These findings suggest that
retinotopic mapping may be fundamental to significant preattentive visual
processes.

通过整合视网膜映射到深度卷积神经网络中，可以提高图像分类和定位性能，特别是对于孤立对象的图像缩放和旋转。此外，视网膜映射网络在分类任务上取得了可比较的性能，并在转换的视网膜中心位移时表现出了改进的分类定位能力。这些发现表明，视网膜映射对于重要的注意前视觉过程可能是基础性的。