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I've been thinking a lot about this sort of thing lately, and isn't it the case that ideally you shouldn't need to manually confirm or reject mismatches? If the
by dTal 7y ago
I've been thinking a lot about this sort of thing lately, and isn't it the case that ideally you shouldn't need to manually confirm or reject mismatches? If the learning program maintains a probability density for "training labels are wrong", definitive ground truth should be unnecessary - eventually it will figure out the mismatches by itself.
As I understand it, this is the core of recursive Bayesian estimation. At the end of the day we don't really have ground truth for anything - it's all filtered through senses with error bars. So any learning process needs to be robust to that.
- MauranKilom 7y agoSure, you can modify your model to better account for bad training data. But you could also fix the training data. The previous comment pointed out that fixing the training data (to a high degree at least, using the predictions of an intermediate version) is significantly faster than the initial labeling.