8,236 research outputs found
Deep unsupervised clustering with Gaussian mixture variational autoencoders
We study a variant of the variational autoencoder model with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the standard variational approach in these models is unsuited for unsupervised clustering, and mitigate this problem by leveraging a principled information-theoretic regularisation term known as consistency violation. Adding this term to the standard variational optimisation objective yields networks with both meaningful internal representations and well-defined clusters. We demonstrate the performance of this scheme on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving higher performance on unsupervised clustering classification than previous approaches
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
We address the problem of unsupervised disentanglement of latent
representations learnt via deep generative models. In contrast to current
approaches that operate on the evidence lower bound (ELBO), we argue that
statistical independence in the latent space of VAEs can be enforced in a
principled hierarchical Bayesian manner. To this effect, we augment the
standard VAE with an inverse-Wishart (IW) prior on the covariance matrix of the
latent code. By tuning the IW parameters, we are able to encourage (or
discourage) independence in the learnt latent dimensions. Extensive
experimental results on a range of datasets (2DShapes, 3DChairs, 3DFaces and
CelebA) show our approach to outperform the -VAE and is competitive with
the state-of-the-art FactorVAE. Our approach achieves significantly better
disentanglement and reconstruction on a new dataset (CorrelatedEllipses) which
introduces correlations between the factors of variation.Comment: AAAI-201
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