Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models (1711.05772v2)

Abstract: 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).

• The paper introduces latent constraints to enable post-hoc conditional generation from unconditional VAEs without retraining.
• It employs gradient-based optimization and learned critic functions to balance realistic reconstructions with controlled attribute modifications.
• The approach achieves zero-shot capability for new attributes and preserves identity, as demonstrated on both image and music generation tasks.

Overview of "Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models"

This paper presents a methodology for conditional data generation from pre-trained unconditional generative models, specifically focusing on Variational Autoencoders (VAEs). The primary contribution lies in the introduction of latent constraints that enable conditional sampling without the need for model retraining. These constraints are learned post-hoc and are used to guide the generation of data with specified attributes.

Key Contributions and Methodology

1. Conditional Generation via Latent Constraints: The paper introduces a method for learning critic functions in the latent space of a VAE. These functions, once trained, are able to identify regions that correspond to outputs with desired attributes. Through either gradient-based optimization or the use of a trained actor function, samples can be drawn from these regions to generate conditionally controlled outputs.
2. Balancing Reconstruction and Sample Quality: A universal "realism" constraint is enforced, which requires samples in latent space to appear authentic by being indistinguishable from the encodings of real data rather than simply adhering to the prior. This approach mitigates the typical VAE trade-off between sharp reconstructions and realistic samples.
3. Identity-Preserving Transformations: The paper demonstrates that identity-preserving changes in an object’s attributes can be achieved by making minimal adjustments in the latent space. Through gradient-based optimization, expressions or features such as hair color can be modified while retaining the core identity of an individual in an image.
4. Zero-shot Conditional Generation: In the absence of labeled data, the authors propose a zero-shot learning strategy where rule-based constraints are used to guide the construction of latent constraints. This allows for conditional generation even for new attributes or in cases where a differentiable reward function is not feasible.

These methods are exemplified through tasks involving image manipulation and music note sequence generation. The approach enables dynamic and customizable usage of VAEs, showing flexibility in generating diverse outputs based on user-defined attributes or constraints.

Experimental Insights

Extensive experiments on the CelebA dataset illustrate the efficacy of imposing attribute constraints in latent spaces for generating conditionally controlled images. The results show the model's capability in preserving identity, achieving accurate attribute modifications, and utilizing zero-shot learning effectively.

• The method achieves high precision and recall for controlled attribute generation, comparable to or exceeding other conditional generative models like Conditional GANs (CGANs).
• The latent constraint approach is computationally efficient as it bypasses model retraining, thus providing a scalable solution for generating data based on new criteria or rules.

Implications and Future Directions

This work opens several avenues for further research in making generative models more adaptable and responsive to user inputs with minimal training alterations. The ability to impose constraints post-hoc enriches the usability of pre-trained models and broadens applicability in fields requiring extensive customization, such as interactive media, design, and automated content creation.

Moreover, the proposed approach highlights potential in integrating more complex and human-like understanding of content creation rules, thereby contributing to advancements in interactive AI systems. Future developments could explore deeper integration of these methods in real-time applications or expand on optimizing latent space explorations to manage increasingly complex datasets or generative tasks.