Improving Text to Image Generation using Mode-seeking Function
This addresses mode collapse in text-to-image synthesis for AI researchers, though it is incremental as it builds on existing GAN frameworks.
The paper tackled mode collapsing in text-to-image GANs by introducing a mode-seeking loss function to differentiate latent space points for distinct image generation, achieving strong performance on CUB and COCO datasets compared to state-of-the-art methods.
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.