The ability of generative models to accurately fit data distributions has
resulted in their widespread adoption and success in fields such as computer
vision and natural language processing. In this chapter, we provide a brief
overview of the application of generative models in the domain of infrared (IR)
image super-resolution, including a discussion of the various challenges and
adversarial training methods employed. We propose potential areas for further
investigation and advancement in the application of generative models for IR
image super-resolution.

生成模型在红外图像超分辨率应用中的潜在领域和挑战，以及采用的对抗训练方法的简要概述。

基于 GAN 的红外图像超分辨率

Infrared Image Super-Resolution via GAN

Computational design problems arise in a number of settings, from synthetic
biology to computer architectures. In this paper, we aim to solve data-driven
model-based optimization (MBO) problems, where the goal is to find a design
input that maximizes an unknown objective function provided access to only a
static dataset of prior experiments. Such data-driven optimization procedures
are the only practical methods in many real-world domains where active data
collection is expensive (e.g., when optimizing over proteins) or dangerous
(e.g., when optimizing over aircraft designs). Typical methods for MBO that
optimize the design against a learned model suffer from distributional shift:
it is easy to find a design that "fools" the model into predicting a high
value. To overcome this, we propose conservative objective models (COMs), a
method that learns a model of the objective function that lower bounds the
actual value of the ground-truth objective on out-of-distribution inputs, and
uses it for optimization. Structurally, COMs resemble adversarial training
methods used to overcome adversarial examples. COMs are simple to implement and
outperform a number of existing methods on a wide range of MBO problems,
including optimizing protein sequences, robot morphologies, neural network
weights, and superconducting materials.

本文旨在解决基于数据驱动的基于模型优化 (MBO) 问题，提出了一种名为保守目标模型 (COMs) 的方法，该方法使用对无法分布的输入提供目标函数的下界的模型进行优化， 结构上，COMs 类似于用于克服对抗性示例的对抗性训练方法，并在广泛的 MBO 问题上优于许多现有方法。

有效离线基于模型优化的保守目标模型

Conservative Objective Models for Effective Offline Model-Based  Optimization

Graph neural networks (GNNs) have been successfully exploited in graph
analysis tasks in many real-world applications. The competition between attack
and defense methods also enhances the robustness of GNNs. In this competition,
the development of adversarial training methods put forward higher requirement
for the diversity of attack examples. By contrast, most attack methods with
specific attack strategies are difficult to satisfy such a requirement. To
address this problem, we propose GraphAttacker, a novel generic graph attack
framework that can flexibly adjust the structures and the attack strategies
according to the graph analysis tasks. GraphAttacker generates adversarial
examples through alternate training on three key components: the multi-strategy
attack generator (MAG), the similarity discriminator (SD), and the attack
discriminator (AD), based on the generative adversarial network (GAN).
Furthermore, we introduce a novel similarity modification rate SMR to conduct a
stealthier attack considering the change of node similarity distribution.
Experiments on various benchmark datasets demonstrate that GraphAttacker can
achieve state-of-the-art attack performance on graph analysis tasks of node
classification, graph classification, and link prediction, no matter the
adversarial training is conducted or not. Moreover, we also analyze the unique
characteristics of each task and their specific response in the unified attack
framework. The project code is available at
this https URL

本研究提出了一种名为 GraphAttacker 的新型通用图形攻击框架，该框架可以根据图形分析任务灵活调整结构和攻击策略，并且能够在没有进行对抗训练的情况下，在节点分类、图分类和链接预测等图形分析任务中实现最先进的攻击性能。