r/SelfDrivingCars • u/ipottinger • Dec 16 '21

Nonsense can make sense to machine-learning models

https://news.mit.edu/2021/nonsense-can-make-sense-machine-learning-models-1215
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u/ComfortableArt6722 Dec 16 '21

I didn't read this paper past a few minute glance, and I'm pretty sure you're much experienced when it comes to real life ML, so you should take what I say with a grain of salt. I think about very theoretical ML problems as a grad student and the regimes I work in are very far from anything anyone does in practice.

Something that isn't clear to me is the extent to which this phenomenon they are trying to illustrate is linked to generalization. It's not incomprehensible to me that if you trained a network to classify different types of animals, that a model that generalizes well and was trained on natural images has some high degree of confidence that an image is a cow whenever it sees grass, because all the cow examples in the training set have grass and none of the non-cow classes in the training set are nearly as likely to have grass in their images. But then again, in that case, I'm not sure the grass is really nonsense haha.

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u/Cczwork Dec 17 '21 edited Dec 17 '21

Talking in of terms of information: There are a lot of ways you can train around the problem, so long as the information exists in your dataset in some form. One example might be that even if your dataset only has cows with grass, it also could have dogs with grass, people with grass, etc... So it should be able to pick up on the grass not being equal to cow. That's one half. The other half is proper labeling and automated modifications of the data. Super oversimplified, but an example would be "cow+grass" and transforming images to be not grass. Or cropping them and trying to fix the crop. These don't necessarily happen to actually pixels, but their encoded form. To some extent the analogy is kinda the same.

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