Newer text-to-image models produce less diverse synthetic data, causing classifiers trained solely on them to show declining accuracy on real test sets.
Fake it till you make it: Learning trans- ferable representations from synthetic imagenet clones
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When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators
Newer text-to-image models produce less diverse synthetic data, causing classifiers trained solely on them to show declining accuracy on real test sets.