2016
NeurIPS
NeurIPS 2016
Towards Conceptual Compression
Abstract
We introduce convolutional DRAW, a homogeneous deep generative model achieving state-of-the-art performance in latent variable image modeling. The algorithm naturally stratifies information into higher and lower level details, creating abstract features and as such addressing one of the fundamentally desired properties of representation learning. Furthermore, the hierarchical ordering of its latents creates the opportunity to selectively store global information about an image, yielding a high quality 'conceptual compression' framework.
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Trend Setter
- Computer Vision
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Hot Topic Early Bird
- deep generative model
Topics
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Deep Learning > Models > Generative Models
Deep Learning > Learning Types > Representation Learning
Deep Learning > Application Areas > Computer Vision
Deep Learning > Models > Variational Autoencoders