  Deep generative neural networks have proven effective at both conditional and
unconditional modeling of complex data distributions. Conditional generation
enables interactive control, but creating new controls often requires expensive
retraining. In this paper, we develop a method to condition generation without
retraining the model. By post-hoc learning latent constraints, value functions
that identify regions in latent space that generate outputs with desired
attributes, we can conditionally sample from these regions with gradient-based
optimization or amortized actor functions. Combining attribute constraints with
a universal "realism" constraint, which enforces similarity to the data
distribution, we generate realistic conditional images from an unconditional
variational autoencoder. Further, using gradient-based optimization, we
demonstrate identity-preserving transformations that make the minimal
adjustment in latent space to modify the attributes of an image. Finally, with
discrete sequences of musical notes, we demonstrate zero-shot conditional
generation, learning latent constraints in the absence of labeled data or a
differentiable reward function. Code with dedicated cloud instance has been
made publicly available (https://goo.gl/STGMGx).
