They are reasonably competitive with GANs. I haven't kept up on the latest models on either side, but VAEs have historically tended to be a little blurrier than GANs.
I think VAE's haven't been the state of the art since around 2016-2017? They have been squeezed from both directions, autoregressive models on the compression side, GAN's on the generation side.
They are still fairly competitive on both sides though.
Yeah, I guess I was thinking of VQVAE as a state-of-the-art example, but it was indeed 2017. Time flies! It's still pretty influential on newer systems though, e.g. OpenAI's DALL-E that made waves earlier this year has a VAE component (in addition to a Transformer component).
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For people who want a more stats-grounded approach, VAEs are more or less state of the art these days: https://en.wikipedia.org/wiki/Variational_autoencoder
They are reasonably competitive with GANs. I haven't kept up on the latest models on either side, but VAEs have historically tended to be a little blurrier than GANs.
I think VAE's haven't been the state of the art since around 2016-2017? They have been squeezed from both directions, autoregressive models on the compression side, GAN's on the generation side.
They are still fairly competitive on both sides though.
Yeah, I guess I was thinking of VQVAE as a state-of-the-art example, but it was indeed 2017. Time flies! It's still pretty influential on newer systems though, e.g. OpenAI's DALL-E that made waves earlier this year has a VAE component (in addition to a Transformer component).