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Title: Generating Diverse High-Resolution Images with VQ-VAE

Paper content:
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation.
To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before.
We use simple feed-forward encoder and decoder networks, thus our model is an attractive candidate for applications where the encoding and decoding speed is critical.
Additionally, this  allows us to only sample autoregressively in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images.
We demonstrate that a multi-scale hierarchical organization of  VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.
Deep generative models have significantly improved in the past few years [1; 18; 17] .
This is, in part, thanks to architectural innovations as well as computation advances that allows training them at larger scale in both amount of data and model size.
The samples generated from these models are hard to distinguish from real data without close inspection, and their applications range from super resolution BID14 to domain editing BID31 , artistic manipulation BID23 , or text-to-speech and music generation BID16 .We
distinguish two main types of generative models: maximum likelihood based models, which include VAEs [11; 21] , flow based [5; 20; 6; 12] and autoregressive models [14; 27] ; and implicit generative models such as Generative Adversarial Networks (GANs) BID7 . Each
of these models offer several trade-offs such as sample quality, diversity, speed, etc. GANs
optimize a minimax objective with a generator neural network producing images by mapping random noise onto an image, and a discriminator defining the generators' loss function by classifying its samples as real or fake. Larger
scale GAN models can now generate high-quality and highresolution images [1; 10] . However
, it is well known that samples from these models do not fully capture the diversity of the true distribution. Furthermore
, GANs are challenging to evaluate, and a satisfactory generalization measure on a test set to assess overfitting does not yet exist. For model
comparison and selection, researchers have used image samples or proxy measures of image quality such as Inception Score (IS) BID22 and Fréchet Inception Distance (FID) BID8 .In contrast
, likelihood based methods optimize negative log-likelihood (NLL) of the training data. This objective
allows model-comparison and measuring generalization to unseen data. Additionally,
since the probability that the model assigns to all examples in the training set is maximized, likelihood based models, in principle, cover all modes of the data, and do not suffer from the problems of mode collapse and lack of diversity as seen in GANs. In spite of these
advantages, directly maximizing likelihood in the pixel space can be challenging. First, NLL in pixel
space is not always a good measure of sample quality BID24 , and cannot reliably be used to make comparison between different model classes. There is no intrinsic
incentive for these models to focus on, for example, global structure. Some of these issues
are alleviated by introducing inductive biases such as multi-scale [26; 27; 19; 16] or by modeling the dominant bit planes in an image [13; 12] .In this paper we use
ideas from lossy compression to relieve the generative model from modeling negligible information. Indeed, techniques such
as JPEG BID30 have shown that it is often possible to remove more than 80% of the data without noticeably changing the perceived image quality.As proposed by BID28 , we compress images into a discrete latent space by vector-quantizing intermediate representations of an autoencoder. These representations are
over 30x smaller than the original image, but still allow the decoder to reconstruct the images with little distortion. The prior over these discrete
representations can be modeled with a state of the art PixelCNN [27; 28] with selfattention BID29 , called PixelSnail BID2 . When sampling from this prior
, the decoded images also exhibit the same high quality and coherence of the reconstructions (see FIG0 ). Furthermore, the training and
sampling of this generative model over the discrete latent space is also 30x faster than when directly applied to the pixels, allowing us to train on much higher resolution images. Finally, the encoder and decoder
used in this work retains the simplicity and speed of the original VQ-VAE, which means that the proposed method is an attractive solution for situations in which fast, low-overhead encoding and decoding of large images are required.
We propose a simple method for generating diverse high resolution images using VQ-VAE, combining a vector quantized neural representation learning technique inspired by ideas from lossy compression with powerful autoregressive models as priors.
Our encoder and decoder architectures are kept simple and light-weight as in the original VQ-VAE, with the only difference that we propose using hierarchical multi-scale latent maps for larger images.
The improvements seen in the quality of the samples are largely due to the architectural advances in the PixelCNN style priors that more accurately estimate the distribution over the latent space.
In particular, using self-attention seems to be a crucial component for accurately capturing the structure and geometry of objects encoded in the top-level latent map.
We also observe that the quality of our samples is correlated with the improvements in the negative log-likelihood of the model in the latent space, where small gains in likelihood often translate to dramatic improvements in sample quality.
The fidelity of our best class conditional samples are competitive with the state of the art Generative Adversarial Networks, while we see dramatically broader diversity in several classes, contrasting our method against the known limitations of GANs.
We believe our experiments vindicate maximum likelihood in the latent space as a simple and effective objective for learning large scale generative models that do not suffer from the shortcomings of adversarial training.

assistant: text: scale and enhance VQ-VAE with powerful priors to generate near realistic images.