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Clustering methods based on neural networks, called deep clustering methods, leverage the representational power of neural networks to enhance clustering performance. ClusterGan constitutes a generative deep clustering method that exploits generative adversarial networks (GANs) to perform clustering. However, it inherits some deficiencies of GANs, such as mode collapse, vanishing gradients and training instability. In order to tackle those deficiencies, the generative approach of implicit maximum likelihood estimation (IMLE) has been recently proposed. In this paper, we present a clustering method based on generative neural networks, called neural implicit maximum likelihood clustering, which adopts ideas from both ClusterGAN and IMLE. 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