Summary of Content-style Learning From Unaligned Domains: Identifiability Under Unknown Latent Dimensions, by Sagar Shrestha and Xiao Fu
Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions
by Sagar Shrestha, Xiao Fu
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper presents a novel analytical framework, cross-domain latent distribution matching (LDM), which enables identifying latent content and style variables from unaligned multi-domain data without restrictive assumptions. The LDM approach demonstrates that component-wise independence of latent variables is not necessary for identifiability, and prior knowledge of content and style dimensions can be bypassed by imposing sparsity constraints on learned representations. The framework is recast as a regularized multi-domain GAN loss with coupled latent variables, which requires less computational resources while being theoretically equivalent to LDM under mild conditions. Experimental results validate the theoretical claims. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to analyze and identify patterns in data from different sources without knowing much about these patterns beforehand. This is important for tasks like translating between languages or generating new data that looks similar to existing data. The authors develop a new approach called latent distribution matching, which allows them to do this even when the patterns are complex and connected. They also show how to implement their approach using a type of neural network called a GAN. This can be useful for many applications, including image generation and language translation. |
Keywords
» Artificial intelligence » Gan » Image generation » Neural network » Translation
