ComboGAN: Unrestrained Scalability for Image Domain Translation (1712.06909v1)

Abstract: This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs; whereas our model requires only 14 generator/discriminator pairs.

• The paper introduces ComboGAN, a novel model that scales image domain translation linearly by using decoupled encoder-decoder pairs.
• It demonstrates a dramatic reduction in training time (e.g., 220 vs. 2860 hours) while maintaining translation quality comparable to CycleGAN.
• The methodology employs unsupervised cycle-consistency and adversarial training to ensure semantically faithful translations across multiple domains.

Overview of ComboGAN: Unrestrained Scalability for Image Domain Translation

The paper "ComboGAN: Unrestrained Scalability for Image Domain Translation" presents an innovative approach that addresses the scalability limitations inherent in existing domain adaptation frameworks, notably CycleGAN. The authors introduce a new model, ComboGAN, which facilitates efficient image translation across multiple domains while maintaining computational resources and training time at a linear scale with respect to the number of domains. This advancement is significant in light of the quadratic scaling limitations of models like CycleGAN, where additional domains significantly increase the number of generator-discriminator pairs, leading to impractical computational overhead.

Problem Formulation

The traditional setup of image domain translation, exemplified by CycleGAN, involves training separate models for each domain pair. In the case of $n$ domains, this necessitates training $n \choose 2$ separate models, as each pair needs its own CycleGAN model comprised of two generator-discriminator pairs. This results in substantial model proliferation and extensive training times, as demonstrated by the need for 91 different CycleGAN models to handle image translation among 14 artists' styles, requiring a total of 182 generator/discriminator pairs.

Methodology

The core of ComboGAN's approach is to decouple the generator and discriminator components, assigning an encoder-decoder pair to each domain. This structural modification allows for independent translation between any two domains by mixing and matching encoder-decoder pairs, thereby compressing what in CycleGAN would be multiple models into a single versatile architecture. This results in a linear increase in the number of models and training duration with respect to the number of domains.

The authors leverage GANs with adversarial training, which involves a generator $G$ and a discriminator $D$ engaging in a Minimax game. They adapt the CycleGAN model's foundational principles, specifically its unsupervised cycle-consistency loss along with adversarial loss, to set this up in a multi-domain context without changing the underlying objective formulation substantially. Such consistency ensures that translations remain semantically faithful and visually coherent.

Results

ComboGAN was evaluated on two datasets: images of the Alps representing four seasons, and paintings by fourteen different artists. In both cases, the paper shows that ComboGAN not only supports multi-domain scalability with significant reductions in training resource requirements but also maintains translation quality comparable to CycleGAN. This result highlights the effectiveness of ComboGAN as its 14-domain setup required only 14 encoder/decoder pairs versus CycleGAN's 182, maintaining performance while drastically reducing computational time (from 2860 hours to 220 hours).

Theoretical and Practical Implications

The introduction of ComboGAN represents an important theoretical contribution to image domain translation. The decoupling strategy proposed by the authors potentially allows the extension of existing GAN architectures to handle a wider variety of tasks beyond image-to-image translation, particularly where scalability to multiple domains is pertinent. Practically, ComboGAN enables broader accessibility of domain translation applications, potentially reducing the need for high computational resources, thereby democratizing this powerful AI tool.

Speculation on Future Developments

Looking forward, ComboGAN sets a precedent for modular GAN architectures that may lead to further advancements in domain adaptation and style transfer. Future research could investigate the application of ComboGAN-like architectures to other domains such as video translation, augmented reality, and cross-modal translation. Additionally, exploring enhancements such as encoder sharing or intermediate representation regularization could further refine the latent space formulation, enhancing translation accuracy and performance.

The work also invites inquiries into the integration of latent space constraints to ensure robustness, potentially borrowing techniques from variational inference or other regularization schemas to solidify the model's adaptability and efficiency across even broader sets of domains.