Paper revision’s out: The effects of noisy labels on deep convolutional neural networks for music classification

It’s a revision of this paper. It’s a major revision, so major changes! I’ll only take notes on the new stuff.

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The groundtruth are very noisy in tagging dataset. The recall and precision is our (estimates of) evaluation on the groundtruth. Yeah it’s pretty low and we call it ‘groundtruth’…

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Which hurts the performance of them.

Good thing is the trend doesn’t change no matter which groundtruth we use — either the provide one or our re-annotation.

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It’s a figure from Convolutional Recurrent Neural Networks for Music Classification, where I couldn’t get why there’s such differences on the performances per tag. Well, I think I know at least one of the reasons. More noise on tag A → more confusing for the network (whatever the exact structure is) → lower performance.

 

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Why don’t we try to explain other tag category from the same perspective? Yeah, In the dataset, 90s and 00s are majority (84%), but they probably don’t get tagged properly, at least not as good as 60s/70s/80s because come on, you’re in 2010 and listening to 00s music. Why would you tag it? It’s more likely that you would tag 60s/70s/80s music because doing so get you some information. As a result, old tags got less noise, so higher performance. Yes, this is our guess.

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Ok, so with such a corrupted groundtruth, we know what happens when we use it to train. What happens when we use it to evaluate?

(a)(b) : ok it’s fine.

(c) : no it’s not that fine when the differences between the systems are subtle. Which is obvious because at some point, the noise in evaluation > the system-wise differences.

 

That’s it. Please go read it if it sounds interesting! arXiv link here.

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