_:n8798c06f26af4e279b9770aef7c41418xb37
<http://schema.org/description>
"Finding well-defined clusters in data represents a fundamental challenge for\nmany data-driven applications, and largely depends on good data representation.\nDrawing on literature regarding representation learning, studies suggest that\none key characteristic of good latent representations is the ability to produce\nsemantically mixed outputs when decoding linear interpolations of two latent\nrepresentations. We propose the Mixing Consistent Deep Clustering method which\nencourages interpolations to appear realistic while adding the constraint that\ninterpolations of two data points must look like one of the two inputs. By\napplying this training method to various clustering (non-)specific autoencoder\nmodels we found that using the proposed training method systematically changed\nthe structure of learned representations of a model and it improved clustering\nperformance for the tested ACAI, IDEC, and VAE models on the MNIST, SVHN, and\nCIFAR-10 datasets. These outcomes have practical implications for numerous\nreal-world clustering tasks, as it shows that the proposed method can be added\nto existing autoencoders to further improve clustering performance."
<https://deepai.org/profile/mark-hoogendoorn>