论文标题
横向抑制启发的卷积神经网络的复制，以视觉注意和显着性检测
Reproduction of Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection
论文作者
论文摘要
近年来，神经网络继续蓬勃发展，在检测照片中的相关对象或简单地识别（分类）这些对象方面（主要是使用CNN网络）来实现高效率。但是，当前的解决方案远非理想，因为通常事实证明网络可以与自然图像示例有效混淆。我怀疑对象的分类受到对象所在的背景像素的强烈影响。在我的工作中，我将使用此目的的LICNN网络创建的出色显着图分析上述问题。它们旨在抑制所检查对象周围的神经元，从而减少背景像素对分类器预测的贡献。我对自然图像和对抗图像数据集的实验表明，背景与错误分类的前景对象之间存在可见的相关性。人类的经验不支持该网络的这种行为，因为例如，我们不会仅仅因为它在雪地背景上而将黄色的校车与雪犁混淆。
In recent years, neural networks have continued to flourish, achieving high efficiency in detecting relevant objects in photos or simply recognizing (classifying) these objects - mainly using CNN networks. Current solutions, however, are far from ideal, because it often turns out that network can be effectively confused with even natural images examples. I suspect that the classification of an object is strongly influenced by the background pixels on which the object is located. In my work, I analyze the above problem using for this purpose saliency maps created by the LICNN network. They are designed to suppress the neurons surrounding the examined object and, consequently, reduce the contribution of background pixels to the classifier predictions. My experiments on the natural and adversarial images datasets show that, indeed, there is a visible correlation between the background and the wrong-classified foreground object. This behavior of the network is not supported by human experience, because, for example, we do not confuse the yellow school bus with the snow plow just because it is on the snowy background.
