I am trying to simplify a CNN model. Currently, I need to train 3 different models (with the same architecture) to estimate each parameter. I am just wondering if there is a way to just train one model that outputs 3 parameters, with input images containing only single parameter label. Say the three parameters are some kind of measure of symmetry (sym), repetitiveness (rep), roundness (rd). Each of my input images will only give a label for one of the parameters. For each parameter, there is the same number of images. I am thinking if there is a way to accomplish this using some kind of dummy variable to block out the unavailable parameters from backpropagation?
If anyone who has experience with this scenario could give me some advice, it would be super appreciated.