What about findings w. R. T. Online learning? I find continuous learning quality of algorithms to be a topic which often seems to be more of a side-concern, although it carries a lot of relevance in applied settings.
But if I train 10 independent traditional networks, I also won't have newly learned data affect old performance. So in effect they give up the possibility to do transfer learning in exchange for avoiding the disadvantages of transfer learning. But that's a bad tradeoff.
With their approach you always train from scratch, which brings with it the need for huge training data sets.
So I can train a bird classifier on the traditional architecture with 500 labeled images and a pretrained resnet. Our I use a million bird images and this approach.
2) Inputs which are similar in terms of cosine similarity will map to similar (data dependent) products of weight matrices, and thus behave similarly, which of course can affect performance in both good and bad ways. With the results we show on permuted MNIST, its well... just not particularly likely that they will interfere. This is a good thing -- why should completely different data distributions interfere with one another? The point is the method is resiliant to catastrophic forgetting when the cosine similarity between data items from different tasks is small. This highlights the different kind of inductive bias a halfspace gated GLN has compared to a Deep ReLu network.
3) Re bird example, that's slightly unfair. I am sure one could easily make use of the pre-trained resnet to provide informative features to a GLN -- it's early days for this method, hybrid systems haven't been investigated, so I don't know whether it would work better than current SOTA methods for image classification. But I would be pretty confident that some simple combination would work better than chopping the head off a pretrained network and fitting an SVM on top. This is all speculation on my part though. :)
3) I agree that my argument is scientifically unfair. I was trying to argue from the perspective of a prospective user. My customers tend to have a budget limit of how much their classifier is allowed to cost. Training from scratch would be too expensive. But a chopped reset with some conv layers will work OK and be cheap enough.
So for me the user, the ecosystem around your architecture and the availability of pretrained models might make the critical difference on whether I'll use it or not.
Having models constantly re-train as the default setting is essentially what a rolling regression would do - having a rolling regression that doesn't catastrophically forget would be quite valuable.