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。

视网膜映射提高卷积神经网络的鲁棒性

Retinotopic Mapping Enhances the Robustness of Convolutional Neural  Networks

By and large, existing computational models of visual attention tacitly
assume perfect vision and full access to the stimulus and thereby deviate from
foveated biological vision. Moreover, modeling top-down attention is generally
reduced to the integration of semantic features without incorporating the
signal of a high-level visual tasks that have been shown to partially guide
human attention. We propose the Neural Visual Attention (NeVA) algorithm to
generate visual scanpaths in a top-down manner. With our method, we explore the
ability of neural networks on which we impose a biologically-inspired foveated
vision constraint to generate human-like scanpaths without directly training
for this objective. The loss of a neural network performing a downstream visual
task (i.e., classification or reconstruction) flexibly provides top-down
guidance to the scanpath. Extensive experiments show that our method
outperforms state-of-the-art unsupervised human attention models in terms of
similarity to human scanpaths. Additionally, the flexibility of the framework
allows to quantitatively investigate the role of different tasks in the
generated visual behaviors. Finally, we demonstrate the superiority of the
approach in a novel experiment that investigates the utility of scanpaths in
real-world applications, where imperfect viewing conditions are given.

本文提出了神经视觉注意力算法（NeVA），并利用仿生视觉约束在神经网络上实现了类人的扫描路径生成。实验结果表明，该方法在类人扫描路径相似性方面优于现有无监督的人类注意力模型，同时可灵活地研究不同任务对视觉行为的影响，并在考虑不完美的视觉条件下的实际应用中表现出明显的优势。