Yes, the problem is indeed that there's no real way to "look into" a neural network and understand how it has been trained. All you can do is observe that the g

Yes, the problem is indeed that there's no real way to "look into" a neural network and understand how it has been trained. All you can do is observe that the given inputs generate the desired outputs. Even if there was training based on the 2018 frames, that doesn't mean that you have verifiably fixed the problem. It's difficult to train a neural network selectively – every time you "train" the network with additional data, you are increasing the chance that you are also teaching it something you didn't intend which then can have a side-effect in some seemingly unrelated scenario. You can see this in real life with image recognition networks. Teach them too much and they gradually become less effective at identifying anything.