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Poster
Disentangling by Factorising
Hyunjik Kim · Andriy Mnih
We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon beta-VAE by providing a better trade-off between disentanglement and reconstruction quality and being more robust to the number of training iterations. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.
Author Information
Hyunjik Kim (DeepMind, University of Oxford)
Andriy Mnih (DeepMind)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Disentangling by Factorising »
Wed. Jul 11th 12:50 -- 01:00 PM Room A7
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