Offline reinforcement learning (RL) addresses the challenge of expensive and
high-risk data exploration inherent in RL by pre-training policies on vast
amounts of offline data, enabling direct deployment or fine-tuning in
real-world environments. However, this training paradigm can compromise policy
robustness, leading to degraded performance in practical conditions due to
observation perturbations or intentional attacks. While adversarial attacks and
defenses have been extensively studied in deep learning, their application in
offline RL is limited. This paper proposes a framework to enhance the
robustness of offline RL models by leveraging advanced adversarial attacks and
defenses. The framework attacks the actor and critic components by perturbing
observations during training and using adversarial defenses as regularization
to enhance the learned policy. Four attacks and two defenses are introduced and
evaluated on the D4RL benchmark. The results show the vulnerability of both the
actor and critic to attacks and the effectiveness of the defenses in improving
policy robustness. This framework holds promise for enhancing the reliability
of offline RL models in practical scenarios.

该研究提出了一个框架，通过利用先进的对抗攻击和防御来提高离线强化学习模型的稳健性，并以 D4RL 基准进行了评估，结果显示了演员和评论家对攻击的脆弱性以及防御策略在提高策略稳健性方面的有效性，为提高实际场景中离线强化学习模型的可靠性提供了希望。

走向强壮的策略：通过对抗攻击和防守增强离线强化学习

Towards Robust Policy: Enhancing Offline Reinforcement Learning with  Adversarial Attacks and Defenses

Conventional dataset distillation requires significant computational
resources and assumes access to the entire dataset, an assumption impractical
as it presumes all data resides on a central server. In this paper, we focus on
dataset distillation in practical scenarios with access to only a fraction of
the entire dataset. We introduce a novel distillation method that augments the
conventional process by incorporating general model knowledge via the addition
of Deep KKT (DKKT) loss. In practical settings, our approach showed improved
performance compared to the baseline distribution matching distillation method
on the CIFAR-10 dataset. Additionally, we present experimental evidence that
Deep Support Vectors (DSVs) offer unique information to the original
distillation, and their integration results in enhanced performance.

在实际场景中仅能访问整个数据集的一部分的情况下，我们提出了一种新的数据集提炼方法，通过添加深度 KKT（DKKT）损失来增强传统过程中的一般模型知识。在 CIFAR-10 数据集上的实验表明，我们的方法在实际设置下相较于基线分布匹配提炼方法表现出更好的性能，并且我们还呈现了深度支持向量（DSVs）在原始提炼中提供了独特信息的实验证据，它们的整合结果提升了性能。

基于深度支持向量的实用数据集蒸馏

Practical Dataset Distillation Based on Deep Support Vectors

The fusion of a low-spatial-resolution hyperspectral image (LR-HSI) with a
high-spatial-resolution multispectral image (HR-MSI) has garnered increasing
research interest. However, most fusion methods solely focus on the fusion
algorithm itself and overlook the degradation models, which results in
unsatisfactory performance in practical scenarios. To fill this gap, we propose
physics-inspired degradation models (PIDM) to model the degradation of LR-HSI
and HR-MSI, which comprises a spatial degradation network (SpaDN) and a
spectral degradation network (SpeDN). SpaDN and SpeDN are designed based on two
insights. First, we employ spatial warping and spectral modulation operations
to simulate lens aberrations, thereby introducing non-uniformity into the
spatial and spectral degradation processes. Second, we utilize asymmetric
downsampling and parallel downsampling operations to separately reduce the
spatial and spectral resolutions of the images, thus ensuring the matching of
spatial and spectral degradation processes with specific physical
characteristics. Once SpaDN and SpeDN are established, we adopt a
self-supervised training strategy to optimize the network parameters and
provide a plug-and-play solution for fusion methods. Comprehensive experiments
demonstrate that our proposed PIDM can boost the fusion performance of existing
fusion methods in practical scenarios.

我们提出了物理启发的退化模型（PIDM），通过空间降级网络（SpaDN）和光谱降级网络（SpeDN）模拟镜头畸变和图像分辨率降低的过程，从而提高了现有融合方法在实际场景中的融合性能。

物理启发的高光谱图像融合退化模型

Physics-Inspired Degradation Models for Hyperspectral Image Fusion

Creative image animations are attractive in e-commerce applications, where
motion transfer is one of the import ways to generate animations from static
images. However, existing methods rarely transfer motion to objects other than
human body or human face, and even fewer apply motion transfer in practical
scenarios. In this work, we apply motion transfer on the Taobao product images
in real e-commerce scenario to generate creative animations, which are more
attractive than static images and they will bring more benefits. We animate the
Taobao products of dolls, copper running horses and toy dinosaurs based on
motion transfer method for demonstration.

该研究应用运动转移方法在淘宝产品图像中生成创意动画，增强了产品的吸引力和商业利润。