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.

翻译：我们提出了一种新颖的深度聚类方法，将变分自编码器（VAEs）整合到期望最大化（EM）框架中。该方法使用VAE对每个簇的概率分布进行建模，并在通过最大化对数似然的证据下界（ELBO）来更新模型参数与基于学习到的分布优化簇分配之间交替进行。这实现了有效的聚类以及从各簇生成新样本的能力。与现有基于VAE的方法不同，我们的方法无需高斯混合模型（GMM）先验或额外的正则化技术。在MNIST和FashionMNIST数据集上的实验表明，相较于现有先进方法，本方法具有更优的聚类性能。