Neural Network Loss Landscapes: What do we know? (2021)(damueller.com) |
Neural Network Loss Landscapes: What do we know? (2021)(damueller.com) |
This means that, at the very least, there are many global optima (well, unless all permutable weights end up with the same value, which is obviously not the case). The fact that different initializations/early training steps can end up in different but equivalent optima follows directly from this symmetry. But whether all their basins are connected, or whether there are just multiple equivalent basins, is much less clear. The "non-linear" connection stuff does seem to imply that they are all in some (high-dimensional, non-linear) valley.
To be clear, this is just me looking at these results from the "permutation" perspective above, because it leads to a few obvious conclusions. But I am not qualified to judge which of these results are more or less profound.
The different solutions found in different runs likely share a lot of information, but learn some different things on the edges. It would be cool to isolate the difference between two networks...
http://evolvingstuff.blogspot.com/2011/02/animated-fractal-f...
These are related to recurrent neural networks evolved to maximize fitness whilst wandering through a randomly generated maze and picking up food pellets (the advantage being to remember not to revisit where you have already been.)
That's the researchers who prefer these solutions, not the networks. And that's how the networks find them: because the experimenters have access to the test data and they keep tuning their networks' parameterers until they perfectly fit not only the training, but also the _test_ data.
In that sense the testing data is not "unseen". The neural net doesn't "see" it during training but the researchers do and they can try to improve the network's performance on it, because they control everything about how the network is trained, when it stops training etc etc.
It's nothing to do with loss functions and the answers are not in the maths. It's good, old researcher bias and it has to be controlled by othear means, namely, rigorous design _and description_ of experiments.
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes
Lei Wu, Zhanxing Zhu, Weinan E
https://arxiv.org/abs/1706.10239
I think it was the first paper to study the volume of the basins of attraction of good global minima and used the poisoning scheme to highlight the frequency of bad global minima that are typically not reachable found via SGD on the original dataset without poisoning.
In small (two or three) dimensions, there are ways of visualizing overtraining/regularization/generalization with scatter plots (maybe coloured with output label) of activations in each layer. Training will form tighter "modes" in the activations, and the "low density" space between modes constitutes "undefined input space" to subsequent layers. Overtraining is when real data falls in these "dead" regions. The aim of regularization is to shape the activation distributions such that unseen data falls somewhere with non-zero density.
Training loss does not give any information on generalization here unless it shows you're in a narrow "well". The loss landscapes are high-dimensional and non-obvious to reason about, even in tiny examples.
With the randomly labelled dataset these activation "modes" are essentially gerrymandered to fit the data since the datapoints have no common features correlated to the labels to cause it to do otherwise.
With the meaningfully labelled dataset, and a smooth loss landscape, multiple datapoints with common features & labels will be pushing these activation modes in the same direction creating "high density modes" within which meaningful generalization occurs.
Generalization, or lack of it, is of course also intimately related to adversarial attacks. It seems that what is going on there is that these high density modes are only disconnected from each other (by areas of low density) when considering the degrees of freedom of data on the training set manifold. In the unconstrained input space off the natural data manifold, these high density areas of different generalization are likely to be connected and it's easy to select an "unnatural feature" that will push a datapoint from mapping to one mode to another.
I've suggested this explanation of generalization a number of times over the years, and always had negative feedback from folk who think there's more to the "generalization mystery" than this.
"Why might SGD prefer basins that are flatter?" It's because they look at the derivative. When the bottom of the valley is flat they don't have enough momentum to get out.
I have observed the lottery ticket hypothesis.
https://blockgeni.com/how-to-hill-climb-the-test-set-for-mac...
One benchmark I know where the test set is completely hidden is François Chollet's ARC dataset, and that's done precisely to preclude overfitting to the test set.
It appears (somewhat) generalizing models are easier to compute than models that do not generalize at all.
You'll have to clarify this because I'm not sure what you mean by "real world data". Do you mean e.g. data that is made available after a machine learning system is deployed "live"?
As far as I can tell, nobody really does this kind of "extrinsic" evaluation systematically, first of all because it is very expensive: such "real world data" is unlabelled, and must be labelled before the evaluation.
What's more, the "real world data" is very likely to change between deployments of a machine learning system so any evaluation of a model trained last month may not be valid this month.
So this is all basically very expensive in terms of both money and effort (so, money), and so nobody does it. Instead everyone relies on the approximation of real-world performance on their already labelled datasets.
It's widely recognized that image recognition models typically perform well also on such data. We don't need to quantify that exactly to conclude that many large (in terms of parameters) models generalize quite well to data neither in the training or the test set.
Provided that the model space is large enough to contain both models that generalize well and models that don't (while still fitting the training data), some explanation why we tend to find generalizing models is required.
>> It's widely recognized that image recognition models typically perform well also on such data. We don't need to quantify that exactly to conclude that many large (in terms of parameters) models generalize quite well to data neither in the training or the test set.
I disagree. That is exactly what we need to quantify with great care, precisely because if it were true, an explanation would be needed.
As I say above, and as far as I'm aware, nobody bothers to do this quantification and so any "widely recognised" idea that models generalise to unseen data is only hearsay.