<p>I wouldn't go to add noise to your data. Some papers employ input deformations during training to increase robutness and convergence speed of models. However, these deformations are statistically inefficient (not just on image but any kind of data). 
You can read <a href="https://arxiv.org/pdf/1312.6199v1.pdf" rel="nofollow noreferrer">Intriguing properties of Neural Networks</a> from Szegedy et <em>al.</em> for more details (and refer to references 9 &amp; 13 for papers that uses deformations).</p>

<p>If you want to avoid overfitting, you might be interested to read about <a href="http://www.deeplearningbook.org/contents/regularization.html" rel="nofollow noreferrer">regularization</a> instead.</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>