There's a spectrum of generalization-memorization. The extreme case of memorization is that it would only correctly classify images with precisely identical pixel values to the ones in the training set. When we say that people have "memorized" something, we are still far more general than that. We might key off a few words, say.

The behavior of highly overparameterized networks is basically what you suggest: they will memorize if needed (which you can test by randomizing labels) but will usually generalize better when possible.

> is it possible a network could optimize training loss without memorization when possible

What does that even mean? ML is simply a set of tools to minimize (f(x_i) - y_i)^2 (if you don't like squared loss, pick whatever you prefer). In particular, f() here is our neural network. There is no loss without previous data. The only thing the network is trying to do is "memorize" old data.

It depends of course how you are defining memorization, but the network doesn't necessarily need to use the entirety of every input to do what you are describing. I would think what people mean when they say "it isn't learning, just memorizing" is that the vast majority of information about all previous inputs is being directly encoded in the network.

The person I was responding to mentioned training on random labels, and if training still goes well the network must be a memorizer. But I don't see why it couldn't be the case that a network is able to act as a memorizer, but doesn't if there are certain patterns it can generalize on in the training data.

Also, there is no human learning without previous data either, but I wouldn't characterize all of human learning as memorization.

I now understand what you were trying to say. I think it is still wrong to think of it as "memorization vs. generalization". In my opinion, the question is "why does it happen to generalize when it is in the process of memorization?" As alluded to in another comment, my belief is that the dimensionality of neural networks combined with certain optimization schemes favor networks whose outputs aren't overly sensitive to changes in the data. That is basically the definition of generalization. It also explains why double descent occurs. First, it memorizes as best as it can since this is "easy", then the optimization scheme starts to push it towards parameters that yield better generalizability.

I don't understand the random label training part. Presumably you train on randomised labels which have no relationship with the input but surely it won't generalise well at all given the small probability of predicting the labels correctly by chance (The setup for a Permutation test am I wrong)?

This was the thing I misread first time around.

If you look at Table 1, you see that the models manage to train almost 100% correctly on the randomized labels, but crucially the control test score is down in the 10% region. This is in stark comparison to roughly 80-90% test score for the properly labeled data.

So it seems to me that when faced with structured data they manage to generalize the structure somehow, while when faced with random training data they're powerful enough to simply memorize the training data.

edit: so just point point out, obviously it's to be expected the test to be bad for random input, after all how can you properly test classification of random data?

So the point, as I understand it, isn't that the randomized input leads to poor test results, but rather that the non-randomized ones manages to generalize despite it being capable of simply memorizing the input.

AFAIK that's right, it would be very unlikely to generalize on random labels, which is why I read the comment as suggesting the network shouldn't have low training loss in that situation.