Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

Smarter clustering with Gaussian mixture autoencoders

Imagine your photos or numbers grouped correctly without any labels, like magic but real.
We used a kind of deep model called a variational autoencoder that assumes data fits a mix of simple blobs, a Gaussian mixture, so it can discover groups on its own.
Sometimes the model shrinks many groups into one, a problem known as cluster degeneracy, where distinct things get lumped together.

To stop that we apply a small trick named minimum information constraint which keeps the model from giving up too soon.
The trick helps the model explore different groupings and make clusters more clear.
We tested the idea on simple shapes, handwritten digits and street numbers datasets and the clusters were easy to understand and separate, performance looked strong compared to others.
This means computers can sort unlabeled data better, without heavy tuning or lots of guesses.
Try picturing your messy folder suddenly organized — that the kind of change this method can bring.

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Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

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