Let's say I've trained a CNN that is predicting/inferring live samples that it hasn't seen before. In the event the network makes a correct prediction, would including this as a new sample in its training set increase the model accuracy even further when re-training the network?
I'm unsure about this since it seems as though the network has already learnt the necessary features for making the correct prediction, so adding it as a new training sample might be redundant. On the other hand it might also reinforce to the network that it's on the right track, perhaps giving it further confidence to generalize with whatever features its learnt in regards to that class, that it might be able to apply to the same class in other images it might otherwise make an incorrect prediction with?
The reason I'm thinking of this is that manually labeling each image is a time-consuming process, however if a simple "Correct/Incorrect" popup box was presented after the network made a live prediction, then it's simply a matter of clicking a single button to generate a new labelled training sample, which would be a far easier labeling task.
So how useful would it be to do something like this?