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PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani,Kundan Kumar,Faruk Ahmed,Adrien Ali Taïga,Francesco Visin,David Vazquez,Aaron Courville +6 more
TL;DR: PixelVAE as mentioned in this paper is a VAE model with an autoregressive decoder based on PixelCNN, which achieves state-of-the-art performance on binarized MNIST, competitive performance on 64x64 ImageNet, and high quality samples on the LSUN bedrooms dataset.
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Abstract: Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64x64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
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Citations
•Posted Content
Decision-Making with Auto-Encoding Variational Bayes
TL;DR: This work describes the error of importance sampling as a function of posterior variance and shows that proposal distributions learned with evidence upper bounds are better than the current state of the art.
An Introduction to Variational Autoencoders.
Diederik P. Kingma,Max Welling +1 more
TL;DR: This work provides an introduction to variational autoencoders and some important extensions, which provide a principled framework for learning deep latent-variable models and corresponding inference models.
•Posted Content
NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat,Jan Kautz +1 more
TL;DR: NVAE is the first successful VAE applied to natural images as large as 256$\times$256 pixels and achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strong baseline on FFHQ.
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•Posted Content
InfoVAE: Information Maximizing Variational Autoencoders
TL;DR: It is shown that this model can significantly improve the quality of the variational posterior and can make effective use of the latent features regardless of the flexibility of the decoding distribution, and it is demonstrated that the models outperform competing approaches on multiple performance metrics.
560
•Posted Content
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Jesse Engel,Cinjon Resnick,Adam Roberts,Sander Dieleman,Douglas Eck,Karen Simonyan,Mohammad Norouzi +6 more
TL;DR: A powerful new WaveNet-style autoencoder model is detailed that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform, and NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets is introduced.
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•Proceedings Article
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