  Humans can effortlessly draw new categories from a single exemplar, a feat
that has long posed a challenge for generative models. However, this gap has
started to close with recent advances in diffusion models. This one-shot
drawing task requires powerful inductive biases that have not been
systematically investigated. Here, we study how different inductive biases
shape the latent space of Latent Diffusion Models (LDMs). Along with standard
LDM regularizers (KL and vector quantization), we explore supervised
regularizations (including classification and prototype-based representation)
and contrastive inductive biases (using SimCLR and redundancy reduction
objectives). We demonstrate that LDMs with redundancy reduction and
prototype-based regularizations produce near-human-like drawings (regarding
both samples' recognizability and originality) -- better mimicking human
perception (as evaluated psychophysically). Overall, our results suggest that
the gap between humans and machines in one-shot drawings is almost closed.
