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Data augmentation can have unwanted consequences. For example, horizontal or vertical flipping, what could be more harmless? You can still recognize stuff when
by gwern 7y ago
Data augmentation can have unwanted consequences. For example, horizontal or vertical flipping, what could be more harmless? You can still recognize stuff when it's upside-down, can't you? It's a great data augmentation... Unless, of course, your dataset happens to involve text or symbols in any way, such as the numbers '6' and '9', or left/right arrows in traffic signs, or such obscure letters as 'd' and 'b'.
- thaumasiotes 7y ago> Unless, of course, your dataset happens to involve text or symbols in any way, such as the numbers '6' and '9', or left/right arrows in traffic signs, or such obscure letters as 'd' and 'b'. If your dataset consists of nothing but isolated 'd's and 'p's in unknown orientation, you won't be able to classify them correctly because that is an impossible task. But it would be more common for your dataset to consist of text rather than isolated letters, and in the context of surrounding text it's easy to determine the correct orientation, and therefore to discriminate 'd' from 'p'.
- gwern 7y agoSo it's not a problem, except when it is. Good to know. Incidentally, how does that work for mirroring, when all that surrounding text gets mirrored too? (Consider the real example of the lolcats generated by Nvidia's StyleGAN, where the text captions are completely wrong, and will always be wrong, because it looks like Cyrillic - due to the horizontal dataflipping StyleGAN has enabled by default...)