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In one of his fastai videos Jeremy Howard makes the point that wrong labels can act as regularization and you shouldn't worry too much about them. I'm a bit ske
by s1t5 7y ago
In one of his fastai videos Jeremy Howard makes the point that wrong labels can act as regularization and you shouldn't worry too much about them. I'm a bit skeptical as to how far you can push this but you certainly don't need perfect labelling.
- groar 7y agoThat is true up to a certain point (for instance, in my experience, having bounding boxes that are not pixel-perfect acts as a regularizer), but there is also a good chance that you are mislabelling edge cases, situations that happen rarely, and that definitely hurts the performance of the neural network to make a correct prediction on these difficult / uncommon scenarios.
- kingvash 7y agoWe did some interesting experiments with Go where we inverted the label of who won and measured what impact that had on the final model. This is a binary label so it's probably more impactful (it's the only signal we are measuring) From memory it had only a small impact (2% strength) with ~7% of results flipped, at 4% it was hard to measure the impact (<1%)
- strbean 7y agoAlso, this is applies to mislabeled data in your training set, right? Not a good thing if it is in your test set.