Generative Adversarial Transformers (2103.01209v4)

Abstract: We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linear efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model's strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data-efficiency. Further qualitative and quantitative experiments offer us an insight into the model's inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at https://github.com/dorarad/gansformer.

Essay on Generative Adversarial Transformers

The paper "Generative Adversarial Transformers" investigates the integration of transformer architectures into visual generative modeling through the introduction of the GANformer network. This model is distinguished by its bipartite transformer structure designed to leverage extended interactions across images, with a focus on effective generative processes and high-resolution image synthesis. By iterating information propagation between latent variables and visual features, the GANformer supports the development of compositional representations, enhancing image quality and diversity.

Key Contributions

1. Bipartite Structure: The GANformer innovates on the standard transformer by using a bipartite structure that facilitates scalable long-range interactions while keeping computational costs manageable. This design allows the model to sidestep the quadratic complexity most transformers face.
2. Multiplicative Integration: By adopting a multiplicative integration pattern rather than the traditional additive update mechanism of transformers, the GANformer enhances region-based modulation—a notable distinction from previous architectures such as StyleGAN.
3. Bidirectional Attention: The model employs novel attention mechanisms, referred to as simplex and duplex attention, facilitating both unidirectional (latent-to-image) and bidirectional (latent-to-image and image-to-latent) interactions. This aids in the development of a robust representation of objects and scenes.

Experimental Results

The GANformer model achieves state-of-the-art performance across various datasets, including CLEVR for multi-object scenes and LSUN-Bedrooms and Cityscapes for more complex images. Notably, in generating structured multi-object scenes, the GANformer shows substantial improvements in terms of Fréchet Inception Distance (FID), while necessitating fewer training steps and samples compared to traditional models such as StyleGAN2 and baseline GANs.

Implications for AI

The GANformer demonstrates enhanced interpretability and disentanglement in latent space representations, suggesting its potential application beyond generative tasks. Its efficient modeling of long-range dependencies, crucial for tasks such as scene understanding and synthesis, offers a paradigm that bridges GANs' strengths with transformers’ capabilities in handling relational and compositional data structures. Furthermore, the GANformer’s scalability and efficiency hint at broader applicability in vision-oriented tasks within artificial intelligence.

Future Directions

The paper anticipates extensions of the GANformer to other domains, potentially benefiting tasks that require comprehensive scene understanding or complex relational interactions. As the field advances, exploring alternative architectures inspired by cognitive processes could refine how neural networks model visual perception and other modalities.

In summary, the GANformer presents a significant step in adapting transform-based models for generative tasks, offering a framework that leverages both GANs’ and transformers’ strengths. Its contributions lie in both the theoretical formulation of bipartite attention and practical accomplishments in image synthesis quality, setting a foundation for future exploration in compositional and scalable generative modeling.