The paper claims that gradient ascent on forget samples makes them out-of-distribution for an unlearned image-to-image model, with formal guarantees and a data-poisoning audit.
IEEE Transactions on Neural Networks and Learning Systems (2023)
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Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention
The paper claims that gradient ascent on forget samples makes them out-of-distribution for an unlearned image-to-image model, with formal guarantees and a data-poisoning audit.