Generative adversarial networks (GANs) have demonstrated remarkable potential in the realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional latent space interpolation and manifold (GAN-CLS-INT) encounter challenges generating images that accurately reflect given text descriptions. To overcome these limitations, we introduce TextControlGAN, a controllable GAN-bas...

Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks. However, a more controllable interpretable model is still lacking yet necessary important applications such as assisting training. In this work, we leverage efficient self-attention contrastive learning modules build upon state-of-the-art generative adversarial networks (GANs) to...