Summary of Finding Nemo: Localizing Neurons Responsible For Memorization in Diffusion Models, by Dominik Hintersdorf et al.
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
by Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to prevent diffusion models from memorizing sensitive or copyrighted training images is presented. The authors introduce NeMo, a method that localizes memorization down to the level of neurons in the cross-attention layers of diffusion models. This allows for the deactivation of specific neurons responsible for memorizing particular training samples, preventing the replication of training data at inference time and increasing output diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a superpower that lets computers create super-realistic pictures. Sounds cool, right? But what if this power is used to recreate sensitive or copyrighted images without permission? That’s exactly what’s happening with “diffusion models” – powerful tools that can generate amazing images. The problem is that these models are trained on huge amounts of data from the internet, often without properly attributing or getting consent from content creators. This raises big concerns about privacy and intellectual property. To solve this issue, researchers have come up with a way to “localize” memorization in diffusion models – essentially deleting specific neurons responsible for memorizing particular training samples. This helps prevent unwanted image reproduction and increases the diversity of generated images. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Inference
