论文标题
带有像素嵌入的网络：一种改善图像分类中噪声阻力的方法
Networks with pixels embedding: a method to improve noise resistance in images classification
论文作者
论文摘要
通常，在图像分类的任务中，网络对噪音敏感。例如，带有噪音的猫的图像可能被错误地分类为鸵鸟。通常，为了克服噪声问题，人们使用数据增强技术，也就是说，通过在培训数据集中添加更多带有声音的图像来教导网络来区分噪音。在这项工作中，我们通过引入像素嵌入技术来提供图像分类中的噪声网络。我们使用像素的MNIST数据库在MNIST数据库上使用Pixel嵌入式网络进行测试，该网络缩写为PE的网络。它表明，具有PE的网络在图像上均优于传统网络。像素嵌入的技术可用于许多图像分类任务，以提高噪声阻力。
In the task of image classification, usually, the network is sensitive to noises. For example, an image of cat with noises might be misclassified as an ostrich. Conventionally, to overcome the problem of noises, one uses the technique of data augmentation, that is, to teach the network to distinguish noises by adding more images with noises in the training dataset. In this work, we provide a noise-resistance network in images classification by introducing a technique of pixel embedding. We test the network with pixel embedding, which is abbreviated as the network with PE, on the mnist database of handwritten digits. It shows that the network with PE outperforms the conventional network on images with noises. The technique of pixel embedding can be used in many tasks of image classification to improve noise resistance.
