Summary of Repulsive Latent Score Distillation For Solving Inverse Problems, by Nicolas Zilberstein et al.
Repulsive Latent Score Distillation for Solving Inverse Problems
by Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
First submitted to arxiv on: 24 Jun 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 novel variational framework for posterior sampling is introduced to address mode collapse in Score Distillation Sampling (SDS) when leveraging pre-trained diffusion models in inverse problems and high-dimensional data. The multimodal variational approximation incorporates a repulsion mechanism that promotes diversity among particles by penalizing pairwise kernel-based similarity, serving as a simple regularizer. To mitigate latent space ambiguity, an augmented variational distribution is proposed, disentangling the latent and data spaces. This approach balances computational efficiency, quality, and diversity. Extensive experiments on linear and nonlinear inverse tasks with high-resolution images (512 × 512) using pre-trained Stable Diffusion models demonstrate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make a computer model better at solving problems. They’re working with really big pictures (512 × 512 pixels) and want the model to be more accurate and diverse in its answers. To do this, they’re using a new way of doing calculations that helps the model avoid getting stuck in one “mode” or answer. This method is called Score Distillation Sampling, or SDS for short. The scientists are trying to make it better by adding some extra steps to help the model give more different and accurate answers. |
Keywords
» Artificial intelligence » Distillation » Latent space
