EmbeddGAN: A Novel GAN Framework Using an Embedding Network and Gini Distance Correlation

2026-09-18

AI Summary

为解决GAN训练不稳定和模式崩溃问题，提出EmbeddGAN框架，利用嵌入网络和Gini距离相关性最大化样本与其真假标签间的统计依赖，以提高生成数据质量。

Abstract

Generative Adversarial Networks (GANs) have demonstrated strong performance in generating high-quality synthetic data. However, they are limited by no formal guarantees regarding convergence and the effectiveness of the learning process. In practice, this leads to training instability, mode collapse, and sensitivity to hyperparameters. To address this, we propose EmbeddGAN, a novel adversarial training framework based on a dependence-based objective. Instead of relying on a discriminator that classifies samples as real or fake, EmbeddGAN introduces an embedding network that learns a representation in which statistical dependence between samples and their real/fake labels is maximized, while the generator is trained to minimize this dependence. This objective is implemented using the Gini distance correlation (gCor), which equals zero if and only if the embeddings are statistically independent of the real/fake label. Minimizing this objective therefore encourages real and generated samples to become statistically indistinguishable in the learned embedding space. The embedding network projects both real and generated data into a shared low-dimensional space, where distributional discrepancies can be measured directly through pairwise distances. We adopt a minimax training strategy: the embedding network maximizes the Gini distance correlation (maximizing dependence), while the generator minimizes it (minimizing dependence). Experiments on the MNIST, CIFAR-10, and CelebA datasets demonstrate that EmbeddGAN achieves competitive performance relative to established baselines while exhibiting notably stable training dynamics on the evaluated datasets.

Problem

• Generative Adversarial Networks
• convergence
• training instability
• mode collapse
• hyperparameters

Innovation

• Embedding Network
• Gini Distance Correlation
• Dependence-based Objective