  We present a principled approach to incorporating labels in VAEs that
captures the rich characteristic information associated with those labels.
While prior work has typically conflated these by learning latent variables
that directly correspond to label values, we argue this is contrary to the
intended effect of supervision in VAEs-capturing rich label characteristics
with the latents. For example, we may want to capture the characteristics of a
face that make it look young, rather than just the age of the person. To this
end, we develop the CCVAE, a novel VAE model and concomitant variational
objective which captures label characteristics explicitly in the latent space,
eschewing direct correspondences between label values and latents. Through
judicious structuring of mappings between such characteristic latents and
labels, we show that the CCVAE can effectively learn meaningful representations
of the characteristics of interest across a variety of supervision schemes. In
particular, we show that the CCVAE allows for more effective and more general
interventions to be performed, such as smooth traversals within the
characteristics for a given label, diverse conditional generation, and
transferring characteristics across datapoints.
