Removing duplicates and very near duplicates ought to be one of the first things done to any training dataset...
I wonder why this wasn't done? Too computation heavy?
I wonder why this wasn't done? Too computation heavy?
As to why? Lack of care and vigilance, most likely. If they're willing to spend millions of GPU hours training some of these big models, then this wouldn't be a huge cost, and has near linear complexity over the dataset that parallelises trivially. Sadly, vigilance is a finite resource, and that kind of data cleaning is often dismissed in favour of doubling down on the training and assumming it'll be big enough to handle it (however, as has been shown, that's not the case).
(Edit: thanks FeepingCreature. The deduplication test used for the numbers above was a separate test in the paper for just this, not indicative of their extraction attack in general, with a small corpus and compared a diffusion model trained on both. So I liken it to a litmus test for the efficacy of deduplication.)
But there are lots of ways to identify near identical images algorithmically. Typically the process is to download each image, run it through a neural net to make an image embedding vector (a list of a few hundred floats). Save all those in a database. Then, for each image, if it is too close in 'embed space' to another in the database, then it is a duplicate, and should be removed.
This algorithm might catch 'duplicates' that it shouldn't, like multiple people taking photos of the eiffel tower from the same public viewpoint.
It might miss real duplicates such as an image failing to match with a collage containing the same image.
But it's still better than not removing duplicates at all...
Deduplication, to the best of my knowledge, requires every image be compared to every other image. This is necessarily O(n^2) on n images.
IIRC the training set is 2.3 billion images, if so that's 0.5 * 5.29e18 comparisons[0], which, if done by humans, would require employing literally all humans for approximately a year even if we compared 12 images per second 24/7.
This has to be done computationally, not by humans.
[0] half because (a = b) <=> (b = a)
You can then either have a human check collisions, or just accept a false positive rate and move on.
This is a decent write up of someone doing this on a (smaller) dataset.
https://towardsdatascience.com/detection-of-duplicate-images...
If it’s been changed enough that it hashes to a different value, then it might be reasonable to treat it as a different image. At some point a human is also going to say “that’s not the same.” You can always change your hashing algorithm if you find it’s missing too many dupes.
Regardless, for the domain we’re talking about (deduping training data), a few false negatives should be acceptable.
A large bucket can also be inspected by humans or cut up by applying more perceptual hash function and decreasing tolerance, but it would be counterproductive cheating in this case.
Put simply, do you expect google image search to compare your image to every other possible image? No, they're going to embed it to a vector (512d in the paper) and only compare to probable matches; in the paper they start by brute forcing pairwise comparison of the vectors for the dataset, and then use clique finding to go faster when checking their generated images.