How can I infer no target in a target classification problem based on deep learning? Let's take the MNIST dataset (My application is different) with a lot of noise, I am going to train a deep NN to classify the letters.
What's the right way to infer, there's no letter possibility? or a letter not included in the training set?
Do I have to add the class &quot;not a letter&quot;  and try to pick or simulate the part of the dataset that has no letters but noise,
Or, I can do it without adding the extra label?
What you are looking for is anomaly detection, or novelty detection.

This cannot always be solved by labeling some images as "no letter", since all kind of images having no letters may not be available for labeling, or maybe too costly to exhaustively label.

This link has some directions you can look at for a start.