Networks with pixels embedding: a method to improve noise resistance in images classification
This addresses noise resistance in image classification tasks, but it appears incremental as it builds on existing methods like data augmentation.
The paper tackles the problem of neural networks being sensitive to noise in image classification by introducing a pixel embedding technique, which outperforms conventional networks on noisy images in tests on the MNIST database.
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.