The unlearning goal says: U(D, S, A(D)) ≈ A(D◦S)
Meaning: for a request S to delete/add data in set D, your “unlearning algorithm” U should produce a model U(D, S, A(D)) that looks like a model A(D◦S) re-trained from scratch on dataset D◦S. But does it actually "delete" information requested in S? 👀
Meaning: for a request S to delete/add data in set D, your “unlearning algorithm” U should produce a model U(D, S, A(D)) that looks like a model A(D◦S) re-trained from scratch on dataset D◦S. But does it actually "delete" information requested in S? 👀
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@samdoesarts.bsky.social was among the first big-name illustrators whose style was illegally copied by AI models trained solely on his copyrighted art. These illegal models are still publicly available!
https://huggingface.co/models?search=samdoesart
The answer is NO!
Check out my (slightly technical) paper on why machine unlearning, as it is right now, does not really work!