Improving Text to Image Generation using Mode-seeking Function

Abstract

Generative Adversarial Networks (GANs) have long been used to understand the semantic relationship between the text and image. However, there are problems with mode collapsing in the image generation that causes some preferred output modes. Our aim is to improve the training of the network by using a specialized mode-seeking loss function to avoid this issue. In the text to image synthesis, our loss function differentiates two points in latent space for the generation of distinct images. We validate our model on the Caltech Birds (CUB) dataset and the Microsoft COCO dataset by changing the intensity of the loss function during the training. Experimental results demonstrate that our model works very well compared to some state-of-the-art approaches.

changes : changed the title of the research for submission to CVIU

References in corpus (6)

• Conditional Generative Adversarial Nets
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Mode Regularized Generative Adversarial Networks
• Learning What and Where to Draw
• Diversity-Sensitive Conditional Generative Adversarial Networks
• TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network