With development of deep learning, researchers have developed generative
models in generating realistic images. One of such generative models, a
PixelCNNs model with Vector Quantized Variational AutoEncoder 2 (VQ-VAE-2), can
generate more various images than other models. However, a PixelCNNs model with
VQ-VAE-2, I call it PC-VQ2, requires sufficiently much training data like other
deep learning models. Its practical applications are often limited in domains
where collecting sufficient data is not difficult. To solve the problem,
researchers have recently proposed more data-efficient methods for training
generative models with limited unlabeled data from scratch. However, no such
methods in PC-VQ2s have been researched. This study provides the first step in
this direction, considering generation of images using PC-VQ2s and limited
unlabeled data. In this study, I propose a training strategy for training a
PC-VQ2 with limited data from scratch, phased data augmentation. In the
strategy, ranges of parameters of data augmentation is narrowed in phases
through learning. Quantitative evaluation shows that the phased data
augmentation enables the model with limited data to generate images competitive
with the one with sufficient data in diversity and outperforming it in
fidelity. The evaluation suggests that the proposed method should be useful for
training a PC-VQ2 with limited data efficiently to generate various and natural
images.

通过阶段性数据增强的方式，提出了一种对于有限数据进行有效训练 PixelCNNs 模型的方法，并在数量和质量上优于传统的大数据训练模型。

基于 VQ-VAE-2 和有限数据训练 PixelCNN 的分阶段数据增强

Phased data augmentation for training PixelCNNs with VQ-VAE-2 and  limited data

Semantic inpainting is the task of inferring missing pixels in an image given
surrounding pixels and high level image semantics. Most semantic inpainting
algorithms are deterministic: given an image with missing regions, a single
inpainted image is generated. However, there are often several plausible
inpaintings for a given missing region. In this paper, we propose a method to
perform probabilistic semantic inpainting by building a model, based on
PixelCNNs, that learns a distribution of images conditioned on a subset of
visible pixels. Experiments on the MNIST and CelebA datasets show that our
method produces diverse and realistic inpaintings.

该研究论文提出了一种基于 PixelCNNs 的概率语义修复方法，该方法可以学习有关给定少量像素的图像分布，从而产生多样化和逼真的修复结果，实验证明该方法在 MNIST 和 CelebA 数据集上的表现。

基于限制像素卷积神经网络的概率语义修复

Probabilistic Semantic Inpainting with Pixel Constrained CNNs

PixelCNNs are a recently proposed class of powerful generative models with
tractable likelihood. Here we discuss our implementation of PixelCNNs which we
make available at this https URL Our implementation
contains a number of modifications to the original model that both simplify its
structure and improve its performance. 1) We use a discretized logistic mixture
likelihood on the pixels, rather than a 256-way softmax, which we find to speed
up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels,
simplifying the model structure. 3) We use downsampling to efficiently capture
structure at multiple resolutions. 4) We introduce additional short-cut
connections to further speed up optimization. 5) We regularize the model using
dropout. Finally, we present state-of-the-art log likelihood results on
CIFAR-10 to demonstrate the usefulness of these modifications.

介绍了 PixelCNNs 生成模型的改进实现，使用了离散化的对数混合似然函数、对整个像素进行调节、采用下采样等多种优化方法。在 CIFAR-10 数据集上获得了最先进的对数似然值结果。