  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.
