Summary of Epanechnikov Variational Autoencoder, by Tian Qin et al.
Epanechnikov Variational Autoencoder
by Tian Qin, Wei-Min Huang
First submitted to arxiv on: 21 May 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 This paper bridges the gap between Variational Autoencoders (VAEs) and kernel density estimations (KDEs). It approximates the posterior with KDEs and derives an upper bound of the Kullback-Leibler divergence in the evidence lower bound (ELBO). This allows for the optimization of posteriors in VAEs, addressing limitations of Gaussian latent spaces. The paper shows that under certain conditions, the Epanechnikov kernel is optimal for minimizing the derived upper bound asymptotically. Compared to Gaussian kernels, Epanechnikov has compact support, reducing noise and blur. The implementation of Epanechnikov in ELBO is straightforward. Experiments on MNIST, Fashion-MNIST, CIFAR-10, and CelebA demonstrate the superiority of Epanechnikov Variational Autoencoder (EVAE) over vanilla VAEs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps connect two important ideas in machine learning: Variational Autoencoders (VAEs) and kernel density estimations (KDEs). It shows how to use KDEs to make VAEs better. This makes it possible to improve the quality of reconstructed images, which is important for many applications like image recognition. The paper also finds that a specific type of kernel called Epanechnikov works best. This means that using this kernel can help reduce noise and blur in the images generated by VAEs. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Variational autoencoder
