💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Unlearning in Deep generative models in the context of cutting-edge generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle unlearning tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation


🔸 Presenter: Aryan Komaei

🌀 Abstract:
Diffusion models can unintentionally generate harmful content when trained on unfiltered data. Previous methods tried to address this by adding loss or regularization terms to minimize changes in the model, but balancing content erasure and model stability remains difficult. This paper proposes a novel approach: identifying and preserving "adversarial concepts" — the concepts most affected by parameter changes — to ensure that content erasure has minimal impact on other elements. Their method outperforms current state-of-the-art techniques in maintaining content quality while removing unwanted information.

Session Details:
- 📅 Date: Tuesday
- 🕒 Time: 4:45 - 5:45 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

RIML Lab

💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Unlearning in Deep generative models in the context of cutting-edge generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle unlearning tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation


🔸 Presenter: Aryan Komaei

🌀 Abstract:
Diffusion models can unintentionally generate harmful content when trained on unfiltered data. Previous methods tried to address this by adding loss or regularization terms to minimize changes in the model, but balancing content erasure and model stability remains difficult. This paper proposes a novel approach: identifying and preserving "adversarial concepts" — the concepts most affected by parameter changes — to ensure that content erasure has minimal impact on other elements. Their method outperforms current state-of-the-art techniques in maintaining content quality while removing unwanted information.

Session Details:
- 📅 Date: Tuesday
- 🕒 Time: 4:45 - 5:45 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

arXiv.org

Erasing Undesirable Concepts in Diffusion Models with Adversarial...

Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A practical solution...

hottg.com/RIMLLab/203

1.85K viewsAmir Kasaei, Jun 1 at 07:43