论文标题
强大的CNN到光变化的预科模块
A precortical module for robust CNNs to light variations
论文作者
论文摘要
我们提出了一个简单的数学模型，以考虑到其关键要素：视网膜，侧向基因核（LGN），主要视觉皮层（V1）。图像分类任务中使用的视觉系统的皮质水平与流行CNN的结构之间的类比表明，引入了一种启发的额外的初步卷积模块，该模块受到了前皮层神经元电路的启发，以提高输入图像中的全球光强度和对比度变化方面的鲁棒性。一旦添加了此类额外的模块，我们就在流行的数据库MNIST，FashionMnist和SVHN上验证了我们的假设，就这些变化获得了更大的CNN。
We present a simple mathematical model for the mammalian low visual pathway, taking into account its key elements: retina, lateral geniculate nucleus (LGN), primary visual cortex (V1). The analogies between the cortical level of the visual system and the structure of popular CNNs, used in image classification tasks, suggests the introduction of an additional preliminary convolutional module inspired to precortical neuronal circuits to improve robustness with respect to global light intensity and contrast variations in the input images. We validate our hypothesis on the popular databases MNIST, FashionMNIST and SVHN, obtaining significantly more robust CNNs with respect to these variations, once such extra module is added.
