Summary of Deep Causal Generative Models with Property Control, by Qilong Zhao et al.
Deep Causal Generative Models with Property Control
by Qilong Zhao, Shiyu Wang, Guangji Bai, Bo Pan, Zhaohui Qin, Liang Zhao
First submitted to arxiv on: 25 May 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 The proposed Correlation-aware Causal Variational Auto-encoder (C2VAE) framework tackles the long-standing challenge of jointly identifying key latent variables, their causal relations, and their correlation with properties of interest. This deep generative model simultaneously recovers correlation and causal relationships between properties using disentangled latent vectors. C2VAE learns causality through a structural causal model and correlation via a novel correlation pooling algorithm. The framework demonstrates accurate recovery of true causality and correlation in extensive experiments, outperforming baseline models in controllable data generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary C2VAE is a new way to create data that has the right properties. It’s like solving a puzzle where you find the hidden connections between things. This model is special because it can figure out both how things are connected and how they relate to each other. The results show that C2VAE does a great job of finding these connections and creating new data that matches what we want. |
Keywords
» Artificial intelligence » Encoder » Generative model
