<p>Why don't you want to join the prediction at feature level? </p>

<p>If you really want to stick to your idea of not joining any layers of the network, you can apply a CRF or SVM on top of the overall prediction pipeline to learn cross-correlations between the predictions. For any other method you will need to combine features inside the network, one way or another. However I would strongly recommend, that you consider doing this. It is a general theme in deep learning, that doing stuff inside the network works better then doing it outside.</p>

<p>From what I have learned by experimenting with joint prediction, you will get the most performance gain, if you share weights between all convolutional layers of the network. You can then apply independent fc-layers, followed by a softmax regression and separate loss functions on top of the jointly predicted features. This will allow the network to learn cross-correlation between features while it is still able to make separate predictions.</p>

<p>Have a look at my <a href="https://arxiv.org/abs/1612.07695" rel="nofollow noreferrer">MultiNet paper</a> as a good starting point. All our training code is on <a href="https://github.com/MarvinTeichmann/MultiNet#multinet" rel="nofollow noreferrer">github</a>.</p>
How to jointly learn two tasks at prediction level?
<p>I have trained a network on two different modals of the same image. I pass the data together in one layer but after that, it is pretty much two networks in parallel, they don't share a layer and the two tasks have different set of labels, therefore I have two different loss and accuracy layers (I use caffe btw). I would like to learn these tasks jointly. For example, the prediction of a class of task 1 should be higher in presence of the task 2 predicting a certain class label. I don't want to join them at feature level but at prediction level. How do I get to do this?</p>