# Label-Removed Generative Adversarial Networks Incorporating with K-Means

## TL;DR

This paper introduces KM-GAN, an unsupervised GAN variant that integrates K-Means clustering on discriminator features to generate realistic images without labeled data, achieving results comparable to conditional models.

## Contribution

The paper presents a novel unconditioned GAN framework that incorporates K-Means clustering on discriminator features, reducing reliance on labeled data and improving feature representation.

## Key findings

- KM-GAN produces high-quality images on multiple datasets.
- The model's performance is comparable to conditional GANs.
- Using K-Means enhances discriminator feature representation.

## Abstract

Generative Adversarial Networks (GANs) have achieved great success in generating realistic images. Most of these are conditional models, although acquisition of class labels is expensive and time-consuming in practice. To reduce the dependence on labeled data, we propose an un-conditional generative adversarial model, called K-Means-GAN (KM-GAN), which incorporates the idea of updating centers in K-Means into GANs. Specifically, we redesign the framework of GANs by applying K-Means on the features extracted from the discriminator. With obtained labels from K-Means, we propose new objective functions from the perspective of deep metric learning (DML). Distinct from previous works, the discriminator is treated as a feature extractor rather than a classifier in KM-GAN, meanwhile utilization of K-Means makes features of the discriminator more representative. Experiments are conducted on various datasets, such as MNIST, Fashion-10, CIFAR-10 and CelebA, and show that the quality of samples generated by KM-GAN is comparable to some conditional generative adversarial models.