Neural Discrete Representation Learning
Neural Discrete Representation LearningAaron van den useful representations without supervision remains a key challenge inmachine Learning . In this paper, we propose a simple yet powerful generativemodel that learns such Discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: theencoder network outputs Discrete , rather than continuous, codes; and the prioris learnt rather than static. In order to learn a Discrete latent Representation , weincorporate ideas from vector quantisation (VQ).
31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. arXiv:1711.00937v2 [cs.LG] 30 May 2018 “posterior collapse” issue which has been problematic with many VAE models that have a powerful decoder, often caused by latents being ignored. Additionally, it is the first discrete latent VAE model
Download Neural Discrete Representation Learning
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: