# Improving Text to Image Generation using Mode-seeking Function

Source: [Improving Text to Image Generation using Mode-seeking Function](<https://www.sota2.com/research/paper/68fa68631e209131cea1bb28>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:36:00.183Z

Authors: Naitik Bhise, Zhenfei Zhang, Tien D. Bui

Published: 2020-08-19T12:58:32.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2008.08976>)

[PDF](<https://arxiv.org/pdf/2008.08976.pdf>)