Summary of Moderating the Generalization Of Score-based Generative Model, by Wan Jiang et al.
Moderating the Generalization of Score-based Generative Model
by Wan Jiang, He Wang, Xin Zhang, Dan Guo, Zhaoxin Fan, Yunfeng Diao, Richang Hong
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 recent study on Score-based Generative Models (SGMs) has revealed that while they excel at generating unseen natural data, their generalization abilities also make them susceptible to unintended consequences. To address this issue, researchers examined the widely-used Machine Unlearning (MU) method and found it ineffective for SGMs. The MU approach involves re-training the model after removing unwanted training data, but this doesn’t alter the original score function, which explains its ineffectiveness. Building on this insight, the study proposes a novel Moderated Score-based Generative Model (MSGM) that adjusts the score function to redirect it away from undesirable data during the continuous-time stochastic differential equation process. MSGM demonstrates significant reduction in generating undesirable content while preserving high visual quality for normal image generation. The framework is adaptable to diverse diffusion architectures and training strategies, enabling zero-shot transfer to downstream tasks like image inpainting and reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SGMs are great at creating new images that look real. But this also means they might accidentally create something we don’t want. To fix this problem, researchers looked at a common way to “unlearn” unwanted data and found it doesn’t work for SGMs. They realized that the method isn’t changing the underlying math that makes the model generate new images. So, they came up with a new approach called Moderated Score-based Generative Model (MSGM). MSGM adjusts how the model scores new images to avoid creating unwanted ones while still making great images. This is helpful for lots of applications like filling in missing parts of an image or reconstructing a broken picture. |
Keywords
» Artificial intelligence » Diffusion » Generalization » Generative model » Image generation » Image inpainting » Zero shot
