What are de disadvantages of variationnal auto encoder?
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Variational Auto-Encoders (VAEs) have disadvantages despite their generative power. One major issue is the difficulty in adjusting the size of the latent space, leading to either disentanglement problems or posterior collapse. Traditional training methods often result in learned generative models that ignore latent codes or fail to match the prior distribution over latent space, hindering the model's ability to generate realistic data. Additionally, the decreased interpretability of larger and more complex VAE models limits their generalizability to various domains. Moreover, VAEs may not consistently encode typical samples they can generate, impacting the learned representations. While VAEs excel in efficient feature representation, achieving lossless data reconstruction remains challenging, especially with pure deep learning methods.
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| Papers (4) | Insight |
|---|---|
Open access 03 Feb 2020 | Variational Autoencoders (VAEs) can suffer from issues where the learned generative model may ignore latent codes and fail to match the prior distribution, leading to problematic global optima. |
29 Aug 2022 | Disentanglement issues and posterior collapse are disadvantages of variational auto-encoders, affecting interpretability of latent variables, as discussed in the paper. |
17 Oct 2022 1 Citations | Disadvantages of Variational Auto-Encoders include decreased interpretability for larger models; however, the proposed Cascade VAE in the paper addresses this issue effectively. |
Open access•Posted Content | Not addressed in the paper. |
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