Summary of Symbolic Disentangled Representations For Images, by Alexandr Korchemnyi et al.
Symbolic Disentangled Representations for Images
by Alexandr Korchemnyi, Alexey K. Kovalev, Aleksandr I. Panov
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes ArSyD (Architecture for Symbolic Disentanglement), a novel approach to disentangled representations. Typically, disentangled representations are vectors in latent space, where each coordinate corresponds to one generative factor. However, identifying the specific generative factor can be challenging when dealing with high-dimensional vector representations. In contrast, ArSyD represents each generative factor as a vector of the same dimension as the resulting representation, allowing for symbolic disentangled representations. This is achieved through principles of Hyperdimensional Computing (Vector Symbolic Architectures), where symbols are represented as hypervectors, enabling vector operations on them. The model is trained to reconstruct images in a weakly supervised manner, without making additional assumptions about underlying distributions. Experiments demonstrate ArSyD’s effectiveness on the dSprites and CLEVR datasets, providing a comprehensive analysis of learned symbolic disentangled representations. Additionally, new disentanglement metrics are proposed for comparing methods using latent representations of different dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to edit object properties like color or shape in a controlled way. This paper shows how to achieve this by creating special kinds of computer code called symbolic disentangled representations. These codes allow us to modify objects without changing other aspects, like size or texture. The authors propose a new approach called ArSyD that makes it possible to create these codes. They test their method on images and show that it works well. This means we can use this technology in the future to make computers more intelligent and better at understanding the world around us. |
Keywords
» Artificial intelligence » Latent space » Supervised
