Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models

Understand

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.