paper-conference

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
To help agents reason about scenes in terms of their building blocks, we wish to extract the compositional structure of any given scene …
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew Botvinick, Alexander Lerchner, Christopher P Burgess
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SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
DARLA: Improving zero-shot transfer in reinforcement learning
Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to …
Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher P Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner
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β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce beta-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner.
Irina Higgins, Loic Matthey, Arka Pal, Christopher P Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner
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β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework