Summary of Ddmi: Domain-agnostic Latent Diffusion Models For Synthesizing High-quality Implicit Neural Representations, by Dogyun Park et al.
DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations
by Dogyun Park, Sihyeon Kim, Sojin Lee, Hyunwoo J. Kim
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper proposes a new generative model called Domain-agnostic Latent Diffusion Model for Implicit Neural Representations (DDMI). The authors identify a limitation in existing methods that generate neural network weights and use fixed positional embeddings, which restricts the expressive power of generative models. To overcome this, DDMI generates adaptive positional embeddings instead. The proposed approach combines Discrete-to-continuous space Variational AutoEncoder (D2C-VAE) with hierarchically decomposed positional embeddings for enhanced expressive power. Experimental results demonstrate the versatility and superior performance of DDMI across four modalities and seven benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to generate images, shapes, and videos using something called Implicit Neural Representations (INRs). Currently, these models can’t produce high-quality results because they rely on fixed positions. The researchers propose a new model that generates different positional embeddings for each input, allowing it to capture more details. They tested their approach on various datasets and showed that it performs better than existing methods. |
Keywords
* Artificial intelligence * Diffusion model * Generative model * Neural network * Variational autoencoder
