I am trying to train a binary classification model, but I am not sure how I should think with regards to the background or "other" class.
Is it best to use a multiclass approach with two classes (and thus two output neurons), or should I have a single output neuron?
In the case of a single output neuron, how should I setup the loss function?
What should the background/other training data look like? I guess feeding empty/noise DVS streams are a waste of time since few events mean few generated spikes and thus no output anyway. Therefore I decided to use only negative samples that contain a fair amount of events. Is this correct thinking?
So far I have not found a relevant example to look at. The N-MNIST example contains only the 10 relevant classes and no OTHER class. The Gesture demo trained on DVS-Gesture contains an OTHER GESTURE class but I can't find any information on how it is trained.