# Normalized Diversification

## TL;DR

This paper introduces normalized diversity, a technique to prevent mode collapse in GANs by preserving normalized pairwise distances, leading to more diverse and reliable data generation.

## Contribution

The paper proposes normalized diversity, a novel loss that maintains normalized pairwise distances to enhance diversity and stability in GAN training.

## Key findings

- Improved diversity in generated images
- Reduced mode collapse in GANs
- Enhanced performance in image and pose generation

## Abstract

Generating diverse yet specific data is the goal of the generative adversarial network (GAN), but it suffers from the problem of mode collapse. We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs. The normalized diversification aims to unfold the manifold of unknown topology and non-uniform distribution, which leads to safe interpolation between valid latent variables. By alternating the maximization over the pairwise distance and updating the total distance (normalizer), we encourage the model to actively explore in the high-dimensional output space. We demonstrate that by combining the normalized diversity loss and the adversarial loss, we generate diverse data without suffering from mode collapsing. Experimental results show that our method achieves consistent improvement on unsupervised image generation, conditional image generation and hand pose estimation over strong baselines.