Theoretically, one can train multiple neural mappers
with different representation sizes and choose the one that
bests balances reconstruction and editability for each con-
cept. However, doing so is both cumbersome and imprac-
tical at scale. Instead, we introduce an importance-based
ordering over our final hidden layer h that allows for post-
hoc control of the representation’s dimensionality. This is
achieved by applying a variant of the Nested Dropout tech-
nique proposed in Rippel et al. [30] over the output of h.
Specifically, we uniformly sample a truncation value t and
zero out the outputs of h above the truncation value,

h[i > t] = 0 where t ∼ U (0, dh],

(5)
A Neural Space-Time Representation for Text-to-Image Personalization