user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models

Paper content:
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 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.
Generative modeling of complicated data such as images and audio is a long-standing challenge in machine learning.
While unconditional sampling is an interesting technical problem, it is arguably of limited practical interest in its own right: if one needs a non-specific image (or sound, song, document, etc.) , one can simply pull something at random from the unfathomably vast media databases on the web.
But that naive approach may not work for conditional sampling (i.e., generating data to match a set of user-specified attributes), since as more attributes are specified, it becomes exponentially less likely that a satisfactory example can be pulled from a database.
One might also want to modify some attributes of an object while preserving its core identity.
These are crucial tasks in creative applications, where the typical user desires fine-grained controls BID0 .One
can enforce user-specified constraints at training time, either by training on a curated subset of data or with conditioning variables. These
approaches can be effective if there is enough labeled data available, but they require expensive model retraining for each new set of constraints and may not leverage commonalities between tasks. Deep
latent-variable models, such as Generative Adversarial Networks (GANs; BID8 and Variational Autoencoders (VAEs; BID15 BID26 , learn to unconditionally generate realistic and varied outputs by sampling from a semantically structured latent space. One
might hope to leverage that structure in creating new conditional controls for sampling and transformations BID2 .Here
, we show that new constraints can be enforced post-hoc on pre-trained unsupervised generative models. This
approach removes the need to retrain the model for each new set of constraints, allowing users to more easily define custom behavior. We separate
the problem into (1) creating an unsupervised model that learns how to reconstruct data from latent embeddings, and (2) leveraging the latent structure exposed in that embedding space as a source of prior knowledge, upon which we can impose behavioral constraints.Our key contributions are as follows:Figure 1: (a) Diagram of latent constraints for a VAE. We use one
critic D attr to predict which regions of the latent space will generate outputs with desired attributes, and another critic D realism to predict which regions have high mass under the marginal posterior, q(z), of the training data. (b) We begin
by pretraining a standard VAE, with an emphasis on achieving good reconstructions. (c) To train
the actor-critic pair we use constraint-satisfaction labels, c, to train D to discriminate between encodings of actual data, z ∼ q(z|x), versus latent vectors z ∼ p(z) sampled from the prior or transformed prior samples G(z ∼ p(z), y). Similar
to
a Conditional GAN, both G and D operate on a concatenation of z and a binary attribute vector, y, allowing G to learn conditional mappings in latent space. If G is an
optimizer, a separate attribute discriminator, D attr is trained and the latent vector is optimized to reduce the cost of both D attr and D realism . (d) To sample
from the intersection of these regions, we use either gradient-based optimization or an amortized generator, G, to shift latent samples from either the prior (z ∼ p(z), sampling) or from the data (z ∼ q(z|x), transformation).• We show that
it is possible to generate conditionally from an unconditional model, learning a critic function D(z) in latent space and generating high-value samples with either gradient-based optimization or an amortized actor function G(z), even with a nondifferentiable decoder (e.g., discrete sequences).• Focusing on VAEs
, we address the tradeoff between reconstruction quality and sample quality (without sacrificing diversity) by enforcing a universal "realism" constraint that requires samples in latent space to be indistinguishable from encoded data (rather than prior samples).• Because we start
from a VAE that can reconstruct inputs well, we are able to apply identitypreserving transformations by making the minimal adjustment in latent space needed to satisfy the desired constraints. For example, when
we adjust a person's expression or hair, the result is still clearly identifiable as the same person (see Figure 5 ). This contrasts with
pure GAN-based transformation approaches, which often fail to preserve identity.• Zero-shot conditional
generation. Using samples from the
VAE to generate exemplars, we can learn an actor-critic pair that satisfies user-specified rule-based constraints in the absence of any labeled data.
We have demonstrated a new approach to conditional generation by constraining the latent space of an unconditional generative model.
This approach could be extended in a number of ways.One possibility would be to plug in different architectures, including powerful autoregressive decoders or adversarial decoder costs, as we make no assumptions specific to independent likelihoods.
While we have considered constraints based on implicit density estimation, we could also estimate the constrained distribution directly with an explicit autoregressive model or another variational autoencoder.
The efficacy of autoregressive priors in VAEs is promising for this approach BID16 .
Conditional samples could then be obtained by ancestral sampling, and transformations by using gradient ascent to increase the likelihood under the model.
Active or semisupervised learning approaches could reduce the sample complexity of learning constraints.
Real-time constraint learning would also enable new applications; it might be fruitful to extend the reward approximation of Section 6 to incorporate user preferences as in BID4 .

assistant: text: A new approach to conditional generation by constraining the latent space of an unconditional generative model.