<p>There are a couple of works that deal with this problem. Because you make the training set harder the training error will be lower, however your generalization might be better. It has been shown that adding noise can have stability effects for training Generative Adversarial Networks (<a href="https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_35.pdf?attachauth=ANoY7cobNEKmoWjNBHmgxeMfiyFr0oIuVnlRxknz7M6HSru8m0ELxGAm-mZRawxMB8QAn2TbRt1bnJpXLtTXJUgh8qcikaVHC3LC3B60GkJk-lJMC-amVmxOlUJmeTf3KfIqdcPvLjSCDbAnq2WzBOOl-GxICiFru007Q8nLhvjlffuqfNv5AAIapHMEz71oQ9gvQbrAcWnb_KS47ZAofSYXyyorCGgyE5DtrhDwLYXDidGR4i2ZOOc%3D&amp;attredirects=0" rel="nofollow noreferrer">Adversarial Training</a>).</p>

<p>For classification tasks it is not that cut and dry. Not many works have actually dealt with this topic. The closest one is to my best knowledge is this one from google (<a href="https://arxiv.org/pdf/1412.6572.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1412.6572.pdf</a>), where they show the limitation of using training without noise. They do report a regularization effect, but not actual better results than using other methods.</p>
Adding noise to image for deep learning, yes or no?
<p>I've been thinking that adding noise to an image can prevent overfitting and also "increase" the dataset by adding variations to it. I'm only trying to add some random 1s to images that has shape (256,256,3) which uses uint8 to represent its color. I don't think that can affect the visualization at all (I showed both images with matplotlib and they seems almost the same) and has only ~0.01 mean difference in the sum of their values. </p>

<p>But it doesn't look to have its advances. After training for a long time it's still not as good as the one doesn't use noises.</p>

<p>Has anyone tried to use noise for image classification tasks like this? Is it eventually better?</p>