Variational Inference: A Unified Framework of Generative Models and Some Revelations
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Abstract (arXiv)
We reinterpreting the variational inference in a new perspective. Via this way, we can easily prove that EM algorithm, VAE, GAN, AAE, ALI(BiGAN) are all special cases of variational inference. The proof also reveals the loss of standard GAN is incomplete and it explains why we need to train GAN cautiously. From that, we find out a regularization term to improve stability of GAN training.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jul 2018.
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Citing papers per year: 2022: 2; 2023: 1.
3 papers.
- 2023Diffusion-based Target Sampler for Unsupervised Domain Adaptation17 Mar 2023
- 2022A Mathematical Framework for Learning Probability Distributions22 Dec 2022
- 2022Amortized Variational Inference: A Systematic Review22 Sep 2022