SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders
University of Turin
Status
We are preparing the full page with figures, a method overview, interactive demos, and benchmark results. Check back after the proceedings are published.
Abstract
Concept unlearning in diffusion models is hampered by feature splitting, where concepts are distributed across many latent features, making their removal challenging and computationally expensive. We introduce SAEmnesia, a supervised sparse autoencoder framework that overcomes this by enforcing one-to-one concept-neuron mappings. By systematically labeling concepts during training, our method achieves feature centralization, binding each concept to a single, interpretable neuron. This enables highly targeted and efficient concept erasure. Compared to the state-of-the-art sparse autoencoder-based unlearning approach, SAEmnesia reduces hyperparameter search by 96.67% and achieves a 9.22% improvement on the UnlearnCanvas benchmark for objects. Our method also shows superior scalability in sequential unlearning, improving accuracy by 28.4% when removing nine objects, establishing a step forward for precise and controllable concept erasure. Moreover, SAEmnesia effectively suppresses nudity on the I2P benchmark and remains robust to adversarial attacks.
Citation
@inproceedings{saemnesia2026, title = {SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders}, author = {Enrico Cassano and Riccardo Renzulli and Marco Nurisso and Mirko Zaffaroni and Alan Perotti and Marco Grangetto}, booktitle = {Forty-third International Conference on Machine Learning}, year = {2026}, url = {https://openreview.net/forum?id=Pa6EoOViOq} }