Person re-identification (ReID) remains a challenging task in many real-word
video analytics and surveillance applications, even though state-of-the-art
accuracy has improved considerably with the advent of deep learning (DL) models
trained on large image datasets. Given the shift in distributions that
typically occurs between video data captured from the source and target
domains, and absence of labeled data from the target domain, it is difficult to
adapt a DL model for accurate recognition of target data. We argue that for
pair-wise matchers that rely on metric learning, e.g., Siamese networks for
person ReID, the unsupervised domain adaptation (UDA) objective should consist
in aligning pair-wise dissimilarity between domains, rather than aligning
feature representations. Moreover, dissimilarity representations are more
suitable for designing open-set ReID systems, where identities differ in the
source and target domains. In this paper, we propose a novel
Dissimilarity-based Maximum Mean Discrepancy (D-MMD) loss for aligning
pair-wise distances that can be optimized via gradient descent. From a person
ReID perspective, the evaluation of D-MMD loss is straightforward since the
tracklet information allows to label a distance vector as being either
within-class or between-class. This allows approximating the underlying
distribution of target pair-wise distances for D-MMD loss optimization, and
accordingly align source and target distance distributions. Empirical results
with three challenging benchmark datasets show that the proposed D-MMD loss
decreases as source and domain distributions become more similar. Extensive
experimental evaluation also indicates that UDA methods that rely on the D-MMD
loss can significantly outperform baseline and state-of-the-art UDA methods for
person ReID without the common requirement for data augmentation and/or complex
networks.

本文提出了一种基于最大均值偏差（D-MMD）损失函数的度量学习方法，用于解决人员再识别中的跨域偏移问题和对抗攻击，实验结果表明，该方法在不需要数据增强和 / 或复杂网络的情况下，能够明显优于基准线和现有的无监督域自适应方法。

基于不相似度空间的无监督领域自适应在人员再识别中的应用

Unsupervised Domain Adaptation in the Dissimilarity Space for Person  Re-identification

LHC analyses directly comparing data and simulated events bear the danger of
using first-principle predictions only as a black-box part of event simulation.
We show how simulations, for instance, of detector effects can instead be
inverted using generative networks. This allows us to reconstruct parton level
information from measured events. Our results illustrate how, in general, fully
conditional generative networks can statistically invert Monte Carlo
simulations. As a technical by-product we show how a maximum mean discrepancy
loss can be staggered or cooled.

本文介绍如何使用生成网络逆向模拟探测器效应等过程产生的事件，从而统计上反演蒙特卡罗模拟。此外，还介绍了最大均值失配损失如何被阶段性地或抑制地应用。