Summary of Decoder Ensembling For Learned Latent Geometries, by Stas Syrota et al.
Decoder ensembling for learned latent geometries
by Stas Syrota, Pablo Moreno-Muñoz, Søren Hauberg
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 approach reinterprets Euclidean latent spaces in deep generative models as Riemannian through a pull-back metric, enabling differential geometric analysis of the latent space. This framework is rigorously grounded and empirically valuable for interacting with latent variables. The authors highlight that data manifolds are often compact, disconnected, or filled with holes, leading to topological mismatches with Euclidean latent spaces. A popular solution involves using uncertainty as a proxy for topology, but this is typically achieved through heuristics that lack principle and do not scale well to high-dimensional representations. The authors instead propose using ensembles of decoders to capture model uncertainty and demonstrate how to compute geodesics on the associated expected manifold. Experimental results show that this approach is simple, reliable, and a step towards practical latent geometries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Latent space geometry helps us understand deep generative models better. These models are like super powerful computers that can create new images or text based on what they’ve learned from existing data. But sometimes these models get confused and produce weird results. The authors of this paper found a way to fix this problem by looking at the “geometry” of the model’s internal space, kind of like how we use maps to navigate physical spaces. They also showed that using many different “decoders” (like special calculators) can help us understand the model’s uncertainty and make better predictions. |
Keywords
» Artificial intelligence » Latent space
