GANformer is a novel and efficient type of transformer which can be used for visual generative modeling. The network employs a bipartite structure that enables long-range interactions across an image, while maintaining computation of linearly 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.
Source: Generative Adversarial Transformers
Image source: Generative Adversarial Transformers
Source: Generative Adversarial TransformersPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Image Generation | 2 | 40.00% |
Image Manipulation | 1 | 20.00% |
Disentanglement | 1 | 20.00% |
Scene Generation | 1 | 20.00% |
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |