From KL Divergence to Wasserstein Distance: Enhancing Autoencoders with FID Analysis
Variational Autoencoders (VAEs) are popular Bayesian inference models that excel at approximating complex data distributions in a lower-dimensional latent space. Despite their widespread use, VAEs frequently face challenges in image generation, often resulting in blurry outputs. This outcome is pri...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/139006 |
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