Guided Image Generation with Conditional Invertible Neural Networks (1907.02392v3)

Abstract: In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.

• The paper introduces cINNs, an invertible conditioning architecture that improves training stability and prevents mode collapse in image generation.
• It employs maximum likelihood training to yield diverse and realistic samples, outperforming models like cGANs and cVAEs in key metrics such as MSE and FID.
• Practical applications demonstrated include MNIST digit generation and image colorization, highlighting the model's versatility in resolving ambiguous inputs.

Conditional Invertible Neural Networks for Guided Image Generation

The paper "Guided Image Generation with Conditional Invertible Neural Networks" presents an innovative approach to natural image generation using conditional invertible neural networks (cINNs). Unlike conventional methods such as conditional generative adversarial networks (cGANs) and conditional variational autoencoders (cVAEs), cINNs offer a novel architecture that integrates invertible neural networks (INNs) with unconstrained feed-forward networks for preprocessing conditioning inputs. The architecture's core strengths lie in its stability during training, the absence of mode collapse, the production of diverse samples, and the generation of sharp images without the need for reconstruction losses.

Key Contributions

1. Novel Architecture Design: The paper introduces cINNs, which are conditional versions of INNs. These models preprocess the conditioning input, transforming the task from a purely generative model to one that can be conditioned on auxiliary inputs. The architecture ensures that the resulting model is flexible yet remains invertibly by design, leveraging the bijective nature of INNs to maintain efficient reversibility.
2. Training Stability and Sample Diversity: The proposed maximum likelihood-based training procedure ensures stable training across various datasets without the common pitfalls in similar models, such as mode collapse. INNs inherently support this stability, facilitating the sampling of diverse and realistic images.
3. Practical Applications: The effectiveness of cINNs is demonstrated through MNIST digit generation and image colorization tasks. The ability to generate diverse colorizations from a single grayscale image underscores the model's capacity for resolving ambiguous input data into multiple plausible outputs, showcasing robust generative performance.

Numerical Results and Evaluation

The paper reports strong numerical results benchmarking cINNs against other contemporary models like cGANs and cVAEs. For instance, in image colorization tasks, the cINN model exhibited superior diversity and realism scores, highlighting its efficacy in dealing with complex image conditioning scenarios.

1. Error Metrics: The cINNs minimized the best-of-8 mean square error (MSE) in colorization tasks, outperforming others and maintaining high standards in realism assessed through the Fréchet Inception Distance (FID).
2. Latent Space Manipulation: The paper details cINNs’ emergent properties, such as linearly embedding style attributes, enabling intuitive image manipulations directly in the latent space. This feature widens the scope for applications like content-styled image transfer and style-consistent generative processes.

Implications and Future Directions

The paper’s results suggest promising avenues for improving practical applications in AI, particularly in diverse generation tasks. The proposed cINN architecture is not only a potential alternative to existing methods but may evolve into a pivotal framework for future conditional generative models.

One key implication is the potential impact on tasks that inherently require high diversities, such as semantic segmentation and synthesis in varying image modalities. Moreover, the integration of conditioning networks illustrates the fabric wherein cINNs can be employed across multiple domains, emphasizing their versatility and domain adaptability.

Speculation on Future Developments

Future explorations may focus on scaling cINNs to ultra-high-resolution tasks while maintaining computational efficiency. As further enhancements in conditioning network architectures develop, they may synergize to introduce richer feature extractions, potentially improving the descriptive power of cINNs in conditional scenarios.

Additionally, given the model's stability in handling mode collapse, researchers might find ways to extend cINNs into uncharted territories within generative tasks, refining latent space controls further to encode complex multimodal distributions with ease.

Conclusion

This work on cINNs stands as a significant contribution to the domain of generative modeling, opening up new possibilities for stable, diverse, and high-quality image generation. While challenges remain in scaling and optimizing the conditioning phase, this paper sets a foundation for developing robust models capable of sophisticated image manipulation and generation, providing a compelling argument for the continued investigation and adoption of invertible network architectures in conditional settings.