We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel,
data-driven framework for learning surrogates for parametrized Partial
Differential Equations (PDEs). It consists of a probabilistic, learning
objective in which weighted residuals are used to probe the PDE and provide a
source of {\em virtual} data i.e. the actual PDE never needs to be solved. This
is combined with a physics-aware implicit solver that consists of a much
coarser, discretized version of the original PDE, which provides the requisite
information bottleneck for high-dimensional problems and enables generalization
in out-of-distribution settings (e.g. different boundary conditions). We
demonstrate its capability in the context of random heterogeneous materials
where the input parameters represent the material microstructure. We extend the
framework to multiscale problems and show that a surrogate can be learned for
the effective (homogenized) solution without ever solving the reference
problem. We further demonstrate how the proposed framework can accommodate and
generalize several existing learning objectives and architectures while
yielding probabilistic surrogates that can quantify predictive uncertainty.

提出了物理感知神经隐式求解器（PANIS），这是一种新颖的数据驱动框架，用于学习参数化的偏微分方程（PDE）的代理解。它由一个概率性的学习目标组成，在此目标中，使用加权残差来探测 PDE 并提供源数据，而实际的 PDE 无需解决。与之结合的是一种物理感知隐式求解器，它由原始 PDE 的一个粗糙、离散化版本组成，为高维问题提供必要的信息瓶颈，并在分布不同的情况下实现泛化（例如，不同的边界条件）。演示了在随机异质材料的背景下其能力，在该背景下输入参数表示材料的微观结构。将该框架扩展到多尺度问题，并展示了可以学习到有效（均质化）解的代理且无需解决原问题。此外，还演示了如何适应和泛化几种现有的学习目标和架构，同时产生能够量化预测不确定性的概率性代理解。

物理感知的神经隐式求解器：在异质介质中应用的多尺度、参数化的偏微分方程

Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs  with applications in heterogeneous media

Person re-identification (re-ID) is a challenging problem especially when no
labels are available for training. Although recent deep re-ID methods have
achieved great improvement, it is still difficult to optimize deep re-ID model
without annotations in training data. To address this problem, this study
introduces a novel approach for unsupervised person re-ID by leveraging virtual
and real data. Our approach includes two components: virtual person generation
and training of deep re-ID model. For virtual person generation, we learn a
person generation model and a camera style transfer model using unlabeled real
data to generate virtual persons with different poses and camera styles. The
virtual data is formed as labeled training data, enabling subsequently training
deep re-ID model in supervision. For training of deep re-ID model, we divide it
into three steps: 1) pre-training a coarse re-ID model by using virtual data;
2) collaborative filtering based positive pair mining from the real data; and
3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs
and virtual data. The final re-ID model is achieved by iterating between step 2
and step 3 until convergence. Experimental results on two large-scale datasets,
Market-1501 and DukeMTMC-reID, demonstrate the effectiveness of our approach
and shows that the state of the art is achieved in unsupervised person re-ID.

本文提出了一种无监督人物再识别方法，使用虚拟和真实数据进行训练，产生虚拟数据作为有标记的训练数据，利用协作过滤技术进行正样本挖掘，通过迭代优化来训练模型，实验结果表明该方法在无监督人物再识别方面达到了最先进水平。

利用虚拟和真实人物进行无监督的人员重新识别

Leveraging Virtual and Real Person for Unsupervised Person  Re-identification

In the spectrum of vision-based autonomous driving, vanilla end-to-end models
are not interpretable and suboptimal in performance, while mediated perception
models require additional intermediate representations such as segmentation
masks or detection bounding boxes, whose annotation can be prohibitively
expensive as we move to a larger scale. More critically, all prior works fail
to deal with the notorious domain shift if we were to merge data collected from
different sources, which greatly hinders the model generalization ability. In
this work, we address the above limitations by taking advantage of virtual data
collected from driving simulators, and present DU-drive, an unsupervised
real-to-virtual domain unification framework for end-to-end autonomous driving.
It first transforms real driving data to its less complex counterpart in the
virtual domain and then predicts vehicle control commands from the generated
virtual image. Our framework has three unique advantages: 1) it maps driving
data collected from a variety of source distributions into a unified domain,
effectively eliminating domain shift; 2) the learned virtual representation is
simpler than the input real image and closer in form to the "minimum sufficient
statistic" for the prediction task, which relieves the burden of the
compression phase while optimizing the information bottleneck tradeoff and
leads to superior prediction performance; 3) it takes advantage of annotated
virtual data which is unlimited and free to obtain. Extensive experiments on
two public driving datasets and two driving simulators demonstrate the
performance superiority and interpretive capability of DU-drive.

通过利用虚拟数据来处理自动驾驶的数据合并问题，并通过 DU-drive 实现了端到端自动驾驶的无监督域融合框架，达到了更好的性能表现和解释能力。