We develop Scalable Latent Exploration Score (ScaLES) to mitigate
over-exploration in Latent Space Optimization (LSO), a popular method for
solving black-box discrete optimization problems. LSO utilizes continuous
optimization within the latent space of a Variational Autoencoder (VAE) and is
known to be susceptible to over-exploration, which manifests in unrealistic
solutions that reduce its practicality. ScaLES is an exact and theoretically
motivated method leveraging the trained decoder's approximation of the data
distribution. ScaLES can be calculated with any existing decoder, e.g. from a
VAE, without additional training, architectural changes, or access to the
training data. Our evaluation across five LSO benchmark tasks and three VAE
architectures demonstrates that ScaLES enhances the quality of the solutions
while maintaining high objective values, leading to improvements over existing
solutions. We believe that new avenues to LSO will be opened by ScaLES ability
to identify out of distribution areas, differentiability, and computational
tractability. Open source code for ScaLES is available at
this https URL

我们开发了可扩展的潜在探索评分（ScaLES）来减轻潜在空间优化中的过度探索问题，通过利用训练解码器对数据分布的近似来进行计算，ScaLES 提高了解决方案的质量，同时保持较高的客观值，从而改进了现有的解决方案。

ScaLES：大规模潜在探索分数用于预训练生成网络

ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative  Networks

In this work, we propose to solve ill-posed inverse imaging problems using a
bank of Generative Adversarial Networks (GAN) as a prior and apply our method
to the case of Intrinsic Image Decomposition for faces and materials. Our
method builds on the demonstrated success of GANs to capture complex image
distributions. At the core of our approach is the idea that the latent space of
a GAN is a well-suited optimization domain to solve inverse problems. Given an
input image, we propose to jointly inverse the latent codes of a set of GANs
and combine their outputs to reproduce the input. Contrary to most GAN
inversion methods which are limited to inverting only a single GAN, we
demonstrate that it is possible to maintain distribution priors while inverting
several GANs jointly. We show that our approach is modular, allowing various
forward imaging models, that it can successfully decompose both synthetic and
real images, and provides additional advantages such as leveraging properties
of GAN latent space for image relighting.

该研究提出使用 GAN 作为先验来解决不适定的反演成像问题，并将方法应用于面部和材料的固有图像分解。该方法建立在 GAN 成功捕捉复杂图像分布的基础上，并展示了通过逆转多个 GAN 来维持分布先前的可能性，其是模块化的，可以成功地分解合成和真实图像，并利用 GAN 潜在空间的属性，提供了额外的优势，如图像重照。

JoIN: 联合 GAN 逆向用于固有图像分解

JoIN: Joint GANs Inversion for Intrinsic Image Decomposition

In neural architecture search (NAS) methods based on latent space
optimization (LSO), a deep generative model is trained to embed discrete neural
architectures into a continuous latent space. In this case, different
optimization algorithms that operate in the continuous space can be implemented
to search neural architectures. However, the optimization of latent variables
is challenging for gradient-based LSO since the mapping from the latent space
to the architecture performance is generally non-convex. To tackle this
problem, this paper develops a convexity regularized latent space optimization
(CR-LSO) method, which aims to regularize the learning process of latent space
in order to obtain a convex architecture performance mapping. Specifically,
CR-LSO trains a graph variational autoencoder (G-VAE) to learn the continuous
representations of discrete architectures. Simultaneously, the learning process
of latent space is regularized by the guaranteed convexity of input convex
neural networks (ICNNs). In this way, the G-VAE is forced to learn a convex
mapping from the architecture representation to the architecture performance.
Hereafter, the CR-LSO approximates the performance mapping using the ICNN and
leverages the estimated gradient to optimize neural architecture
representations. Experimental results on three popular NAS benchmarks show that
CR-LSO achieves competitive evaluation results in terms of both computational
complexity and architecture performance.

本文提出了一种凸性正则化潜变量优化（CR-LSO）方法，使用图形变分自动编码器（G-VAE）和输入凸神经网络（ICNNs）实现神经结构搜索，并在三个 NAS 基准测试中获得具有竞争优势的计算和性能结果。