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Top-Down Deep Clustering with Multi-generator GANs
[article]
2021
arXiv
pre-print
Deep clustering (DC) leverages the representation power of deep architectures to learn embedding spaces that are optimal for cluster analysis. This approach filters out low-level information irrelevant for clustering and has proven remarkably successful for high dimensional data spaces. Some DC methods employ Generative Adversarial Networks (GANs), motivated by the powerful latent representations these models are able to learn implicitly. In this work, we propose HC-MGAN, a new technique based
arXiv:2112.03398v2
fatcat:232fy3nryneu3bi4zqaqkoz5zm