What is VAE posterior collapse?

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative. This is often related to a hyperparameter resembling the data variance.

Why is my VAE blurry?

However, the images generated by VAE are blurry. This is caused by the ℓ2 loss, which is based on the assumption that the data follow a single Gaussian distribution. When samples in dataset have multi-modal distribution, VAE cannot generate images with sharp edges and fine details.

Which is better Gan or VAE?

Although both VAE and GANs are very exciting approaches to learn the underlying data distribution using unsupervised learning but GANs yield better results as compared to VAE. In VAE, we optimize the lower variational bound whereas in GAN, there is no such assumption.

What is Elbo VAE?

The evidence lower bound (ELBO, also variational lower bound or negative variational free energy) is a lower bound on the probability of observing some data under a model.

What is a beta VAE?

Beta-VAE is a type of variational autoencoder that seeks to discovered disentangled latent factors. It modifies VAEs with an adjustable hyperparameter that balances latent channel capacity and independence constraints with reconstruction accuracy.

Why is Gan sharper than VAE?

VAEs are theoretically elegant and easy to train. They have nice manifold representations but produce very blurry images that lack details. GANs usually generate much sharper images but face challenges in training stability and sampling diversity, especially when synthesizing high-resolution images.

Is GAN generator similar to VAE encoder?

They are both generative models By rigorous definition, VAE models explicitly learn likelihood distribution P(X|Y) through loss function. GAN does not explicitly learn likelihood distribution. But GAN generators serve to generate images that could fool the discriminator.