does anyone know if it is useful or even possible to train Full Dense Networks, i.e. networks without any convolutional layers using transferlearning?
Unfortunately I haven’t found any sources for this anywhere and I would like to know if it is worthwhile to put some work into it.
By transfer learning you mean that you have an identical network trained on ther task or dataset and you initialize your network with these weights
i want to train my Network with a bunch of (at least similar) simulated Data and then retrain it with a few real measured data.
right know i work with self normalizing neural networks, without any convolutionals layers, full dense only. They work pretty well but i have to start initialized weights with zero mean and a standard deviation of the squared root of 1/(size of input).
So i wonder if it is possible to start with pretrained weights. Searching the web has made me believe that conv layer are a requirement for this, because on every site, they only replace/not freeze the full dense layers behind the conv. layers.
I am not stuck on SNNs, if other (not conv) networks are better suited for transferlearning, I would switch, because I see a big advantage for my application in transferlearning.
Any Experience with this task?