Summary of Disentanglement with Factor Quantized Variational Autoencoders, by Gulcin Baykal et al.
Disentanglement with Factor Quantized Variational Autoencoders
by Gulcin Baykal, Melih Kandemir, Gozde Unal
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper proposes a novel approach to disentangled representation learning, which aims to represent the underlying generative factors of a dataset independently. The authors introduce a discrete variational autoencoder (VAE) based model that learns to represent these factors without prior knowledge. They demonstrate the benefits of using discrete representations over continuous ones in facilitating disentanglement and propose incorporating an inductive bias into the model to further enhance this process. The key innovation is scalar quantization of latent variables with a global codebook, which combines optimization-based disentanglement methods with discrete representation learning. This approach, called FactorQVAE, outperforms previous disentanglement methods on two metrics (DCI and InfoMEC) while maintaining good reconstruction performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to learn about the things that make up our data in a way that makes sense. The researchers created a new kind of model that can do this, called FactorQVAE. It’s like a puzzle piece that helps us figure out what’s going on behind the scenes. They tested it and showed that it works better than other methods at figuring things out. |
Keywords
» Artificial intelligence » Optimization » Quantization » Representation learning » Variational autoencoder
