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To me, the strange thing about talk of "real world generalization" is that the characteristics of this world aren't described. In fact, nothing really delineate

To me, the strange thing about talk of "real world generalization" is that the characteristics of this world aren't described. In fact, nothing really delineates what it is - not that we humans don't have an immediate understanding of it but still. Overall, one idea is experiments on data sets that have structures but where neural network don't generalize well. Or data sets where neural generalize even better than "the real world".

webmaven 6y ago
> To me, the strange thing about talk of "real world generalization" is that the characteristics of this world aren't described. In fact, nothing really delineates what it is - not that we humans don't have an immediate understanding of it but still. But it is defined: In the real world, you have a finite dataset, and so have to use it over and over during training. You can shuffle the order, augment it with transformations, divide it into batches, but ultimately you reuse the whole dataset (other than the subset withheld for evaluation) in each training epoch. If the trained network deals correctly with never-before-seen examples (whether they were examples witheld from the training set or brand new ones), then it has generalized well. Ideally, you wouldn't have to go through any of that rigmarole with a finite dataset, every sample you train on would be fresh and unique, and you would have an endless stream of them. That's the 'ideal world' scenario. The same standard for generalization applies, except that every training sample is never-before-seen as well (so, no epochs).
- joe_the_user 6y ago
  But it is defined: In the real world, you have a finite dataset, and so have to use it over and over during training. You can shuffle the order, augment it with transformations, divide it into batches, but ultimately you reuse the whole dataset (other than the subset withheld for evaluation) in each training epoch. I have no idea how "you have a finite dataset" defines "real world generalization". This line of reasoning seems completely incoherent.
  - webmaven 6y ago
    Sorry, that got scrambled in an edit due to my conflation of "real world generalization" and "real world vs. ideal world training." The result is, indeed, incoherent. Anyway, while "real world vs. ideal world training" is defined, you are correct that generalization is not, at least here. But definitions do exist, and more or less conform to what I wrote. Withholding a subset of training data for validation is meant as a proxy for the entirely new samples that will (presumably) be encountered upon deploying the model, but if the training data is biased or otherwise not representative of the real world data, then it is probable that testing the withheld data won't reveal the problem. In other words, the model generalizes well within the parameters of the training set, but not to the real-world data that has a different distribution, because the training set wasn't representative. There is some interesting discussion of this within Gwern's investigation of the apocryphal 'tank story': https://www.gwern.net/Tanks https://www.gwern.net/Tanks

webmaven 6y ago

> To me, the strange thing about talk of "real world generalization" is that the characteristics of this world aren't described. In fact, nothing really delineates what it is - not that we humans don't have an immediate understanding of it but still. But it is defined: In the real world, you have a finite dataset, and so have to use it over and over during training. You can shuffle the order, augment it with transformations, divide it into batches, but ultimately you reuse the whole dataset (other than the subset withheld for evaluation) in each training epoch. If the trained network deals correctly with never-before-seen examples (whether they were examples witheld from the training set or brand new ones), then it has generalized well. Ideally, you wouldn't have to go through any of that rigmarole with a finite dataset, every sample you train on would be fresh and unique, and you would have an endless stream of them. That's the 'ideal world' scenario. The same standard for generalization applies, except that every training sample is never-before-seen as well (so, no epochs).

joe_the_user 6y ago
But it is defined: In the real world, you have a finite dataset, and so have to use it over and over during training. You can shuffle the order, augment it with transformations, divide it into batches, but ultimately you reuse the whole dataset (other than the subset withheld for evaluation) in each training epoch. I have no idea how "you have a finite dataset" defines "real world generalization". This line of reasoning seems completely incoherent.
- webmaven 6y ago
  Sorry, that got scrambled in an edit due to my conflation of "real world generalization" and "real world vs. ideal world training." The result is, indeed, incoherent. Anyway, while "real world vs. ideal world training" is defined, you are correct that generalization is not, at least here. But definitions do exist, and more or less conform to what I wrote. Withholding a subset of training data for validation is meant as a proxy for the entirely new samples that will (presumably) be encountered upon deploying the model, but if the training data is biased or otherwise not representative of the real world data, then it is probable that testing the withheld data won't reveal the problem. In other words, the model generalizes well within the parameters of the training set, but not to the real-world data that has a different distribution, because the training set wasn't representative. There is some interesting discussion of this within Gwern's investigation of the apocryphal 'tank story': https://www.gwern.net/Tanks https://www.gwern.net/Tanks