LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
This addresses image generation for AI applications, but it is incremental as it builds on existing GAN frameworks.
The paper tackles the problem of generating natural images by incorporating scene structure and context, resulting in more human-recognizable objects compared to DCGAN.
We present LR-GAN: an adversarial image generation model which takes scene structure and context into account. Unlike previous generative adversarial networks (GANs), the proposed GAN learns to generate image background and foregrounds separately and recursively, and stitch the foregrounds on the background in a contextually relevant manner to produce a complete natural image. For each foreground, the model learns to generate its appearance, shape and pose. The whole model is unsupervised, and is trained in an end-to-end manner with gradient descent methods. The experiments demonstrate that LR-GAN can generate more natural images with objects that are more human recognizable than DCGAN.