用VAE替代GMM，让深度聚类更精准高效。
Deep Generative Clustering with VAEs and Expectation-Maximization
- 将VAE嵌入EM框架，用生成模型建模每类分布。
- 在MNIST和FashionMNIST上聚类准确率优于当前最优方法。
- 无需GMM先验或额外正则，简化模型设计。
我们提出一种新型深度聚类方法，将变分自编码器（VAE）融入期望最大化（EM）框架。该方法使用VAE对每个簇的概率分布进行建模，并在最大化对数似然的证据下界（ELBO）以更新模型参数，以及基于学习到的分布优化簇分配之间交替进行。该方法实现了有效的聚类并可从各簇生成新样本。与现有基于VAE的方法不同，本方法无需高斯混合模型（GMM）先验或额外正则化技术。在MNIST和FashionMNIST数据集上的实验表明，其聚类性能优于当前最优方法。
原文摘要 · Abstract (English)
We propose a novel deep clustering method that integrates Variational Autoencoders (VAEs) into the Expectation-Maximization (EM) framework. Our approach models the probability distribution of each cluster with a VAE and alternates between updating model parameters by maximizing the Evidence Lower Bound (ELBO) of the log-likelihood and refining cluster assignments based on the learned distributions. This enables effective clustering and generation of new samples from each cluster. Unlike existing VAE-based methods, our approach eliminates the need for a Gaussian Mixture Model (GMM) prior or additional regularization techniques. Experiments on MNIST and FashionMNIST demonstrate superior clustering performance compared to state-of-the-art methods.
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