Summary of Learning Multimodal Latent Space with Ebm Prior and Mcmc Inference, by Shiyu Yuan et al.
Learning Multimodal Latent Space with EBM Prior and MCMC Inference
by Shiyu Yuan, Carlo Lipizzi, Tian Han
First submitted to arxiv on: 20 Aug 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 new approach to multimodal generative models combines an expressive energy-based model (EBM) prior with Markov Chain Monte Carlo (MCMC) inference, using short-run Langevin dynamics. This method provides a more informative guide for capturing multimodality and improves the learning of shared latent variables for coherent generation across modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to generate images and words that are connected. They used an “energy-based model” as a guide, which helps make sure the generated pictures and text look realistic. Then they used a special kind of math called “Markov Chain Monte Carlo” to make the generation process better. This makes it easier for computers to create new images and text that are related. |
Keywords
» Artificial intelligence » Energy based model » Inference
