What characterises posterior collapse in a VAE?

The decoder likelihood becomes multimodal and the reparameterisation gradient has high variance

The reconstruction loss approaches zero while the approximate posterior becomes deterministic

The aggregate posterior differs substantially from the prior, although individual posteriors remain broad

qφ(z|x) becomes close to p(z) for most x, so the decoder can generate outputs while using little information from z