We experimented with four variants for our NASH model: (i) NASH: with deterministic decoder; (ii) NASH-N: with fixed random noise injected to decoder; (iii) NASH-DN: with data-dependent noise injected to decoder; (iv) NASH-DN-S: NASH-DN with supervised information during training.
the inference (encoder) and generative (decoder) networks are optimized jointly by maximizing a variational lower bound to the marginal distribution of input documents (corpus).