More output neurons than labels? When we train a neural network model for a classification problem, we usually have a dense output layer of size equal to the number of labels we have.

If the layer size was greater, the model can still be trained and have output

How can we interpret such output? are there any applications for this?
The output layer is usually the same size as the last dense layer because we apply a loss function to train the model by comparing the last layer to what the output should be. If your output layer was bigger, it's less intuitive what your loss function should be, but the interpretation of your output would likely come from how you define this loss.