Summary of Safeguard Text-to-image Diffusion Models with Human Feedback Inversion, by Sanghyun Kim et al.
Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion
by Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, Juho Lee
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed framework, Human Feedback Inversion (HFI), aims to mitigate the generation of potentially harmful or copyrighted content by large-scale text-to-image diffusion models. The current models rely on internet-crawled data, which can perpetuate problematic concepts due to incomplete filtration processes. While previous approaches alleviate this issue to some extent, they often require text-specified concepts, introducing challenges in accurately capturing nuanced concepts and aligning model knowledge with human understandings. To address this, HFI condenses human feedback on model-generated images into textual tokens guiding the mitigation or removal of problematic images. This framework can be built upon existing techniques for similar purposes, enhancing their alignment with human judgment. By using a self-distillation-based technique, the training objective is simplified, providing a strong baseline for concept removal. The experimental results demonstrate that HFI significantly reduces objectionable content generation while preserving image quality, contributing to the ethical deployment of AI in the public sphere. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem with artificial intelligence (AI) tools that can create images from text. These tools are great at making nice pictures, but they sometimes make things that shouldn’t exist or might be copied from others without permission. The current way these tools work is flawed because they learn from the internet, which means they can pick up bad ideas and habits. To fix this, the researchers came up with a new approach called Human Feedback Inversion (HFI). HFI takes feedback from humans about the images generated by AI tools and uses it to correct any mistakes or remove unwanted content. This helps ensure that AI-generated images are more accurate and less likely to be harmful. |
Keywords
* Artificial intelligence * Alignment * Diffusion * Distillation
