With the advent of an increasing number of Augmented and Virtual Reality
applications that aim to perform meaningful and controlled style edits on
images of human faces, the impetus for the task of parsing face images to
produce accurate and fine-grained semantic segmentation maps is more than ever
before. Few State of the Art (SOTA) methods which solve this problem, do so by
incorporating priors with respect to facial structure or other face attributes
such as expression and pose in their deep classifier architecture. Our
endeavour in this work is to do away with the priors and complex pre-processing
operations required by SOTA multi-class face segmentation models by reframing
this operation as a downstream task post infusion of disentanglement with
respect to facial semantic regions of interest (ROIs) in the latent space of a
Generative Autoencoder model. We present results for our model's performance on
the CelebAMask-HQ and HELEN datasets. The encoded latent space of our model
achieves significantly higher disentanglement with respect to semantic ROIs
than that of other SOTA works. Moreover, it achieves a 13% faster inference
rate and comparable accuracy with respect to the publicly available SOTA for
the downstream task of semantic segmentation of face images.

本研究通过将 facial semantic ROIs 的语义划分和潜在的 Generative Autoencoder 模型相结合，实现了对面部图像语义细分的优化后任务，并在 CelebAMask-HQ 和 HELEN 数据集上展示了它比当前最先进的 works 具有更好的性能表现。

Latents2Segments：分离生成模型的潜在空间，用于面部图像的语义分割

Latents2Segments: Disentangling the Latent Space of Generative Models  for Semantic Segmentation of Face Images

We present a new generative autoencoder model with dual contradistinctive
losses to improve generative autoencoder that performs simultaneous inference
(reconstruction) and synthesis (sampling). Our model, named dual
contradistinctive generative autoencoder (DC-VAE), integrates an instance-level
discriminative loss (maintaining the instance-level fidelity for the
reconstruction/synthesis) with a set-level adversarial loss (encouraging the
set-level fidelity for there construction/synthesis), both being
contradistinctive. Extensive experimental results by DC-VAE across different
resolutions including 32x32, 64x64, 128x128, and 512x512 are reported. The two
contradistinctive losses in VAE work harmoniously in DC-VAE leading to a
significant qualitative and quantitative performance enhancement over the
baseline VAEs without architectural changes. State-of-the-art or competitive
results among generative autoencoders for image reconstruction, image
synthesis, image interpolation, and representation learning are observed.
DC-VAE is a general-purpose VAE model, applicable to a wide variety of
downstream tasks in computer vision and machine learning.

我们提出了一个名为 DC-VAE 的旨在改善生成自动编码器的新模型，其具有双重对立损失，可以同时执行推理和合成操作。该模型通过将单元级区分性损失和集合级对抗性损失相结合，实现了单元级还原 / 合成的精度和集合级准确度的提高，为计算机视觉和机器学习中的各种下游任务提供了广泛的适用性。

双反差异生成自编码器

Dual Contradistinctive Generative Autoencoder

We introduce a novel generative autoencoder network model that learns to
encode and reconstruct images with high quality and resolution, and supports
smooth random sampling from the latent space of the encoder. Generative
adversarial networks (GANs) are known for their ability to simulate random
high-quality images, but they cannot reconstruct existing images. Previous
works have attempted to extend GANs to support such inference but, so far, have
not delivered satisfactory high-quality results. Instead, we propose the
Progressively Growing Generative Autoencoder (PIONEER) network which achieves
high-quality reconstruction with $128{\times}128$ images without requiring a
GAN discriminator. We merge recent techniques for progressively building up the
parts of the network with the recently introduced adversarial encoder-generator
network. The ability to reconstruct input images is crucial in many real-world
applications, and allows for precise intelligent manipulation of existing
images. We show promising results in image synthesis and inference, with
state-of-the-art results in CelebA inference tasks.

提出了一种新颖的生成自编码器网络模型，能够高质量、高分辨率地学习编码和重建图像，并支持从编码器的潜在空间进行平滑的随机采样。

Pioneer Networks：逐步增长的生成自编码器

Pioneer Networks: Progressively Growing Generative Autoencoder

We introduce a deep, generative autoencoder capable of learning hierarchies
of distributed representations from data. Successive deep stochastic hidden
layers are equipped with autoregressive connections, which enable the model to
be sampled from quickly and exactly via ancestral sampling. We derive an
efficient approximate parameter estimation method based on the minimum
description length (MDL) principle, which can be seen as maximising a
variational lower bound on the log-likelihood, with a feedforward neural
network implementing approximate inference. We demonstrate state-of-the-art
generative performance on a number of classic data sets: several UCI data sets,
MNIST and Atari 2600 games.

本文介绍了一种深度、生成式自编码器，它能够从数据中学习分层式分布式表示。通过基于最小描述长度原理的有效近似参数估计方法，我们证明了该方法在几个经典数据集上取得了最先进的生成性能。