The objective of unsupervised person re-identification (Re-ID) is to learn
discriminative features without labor-intensive identity annotations.
State-of-the-art unsupervised Re-ID methods assign pseudo labels to unlabeled
images in the target domain and learn from these noisy pseudo labels. Recently
introduced Mean Teacher Model is a promising way to mitigate the label noise.
However, during the training, self-ensembled teacher-student networks quickly
converge to a consensus which leads to a local minimum. We explore the
possibility of using an asymmetric structure inside neural network to address
this problem. First, asymmetric branches are proposed to extract features in
different manners, which enhances the feature diversity in appearance
signatures. Then, our proposed cross-branch supervision allows one branch to
get supervision from the other branch, which transfers distinct knowledge and
enhances the weight diversity between teacher and student networks. Extensive
experiments show that our proposed method can significantly surpass the
performance of previous work on both unsupervised domain adaptation and fully
unsupervised Re-ID tasks.

该研究旨在通过使用异构结构和交叉分支监督等提高神经网络中的特征多样性和权重多样性来解决自我合成教师 - 学生网络收敛于局部最小值的问题，从而提高无人监督的人物再识别任务的性能

通过非对称分支增强教师与学生的网络多样性，在无监督个体再识别中应用

Enhancing Diversity in Teacher-Student Networks via Asymmetric branches  for Unsupervised Person Re-identification

While standard persistent homology has been successful in extracting
information from metric datasets, its applicability to more general data, e.g.
directed networks, is hindered by its natural insensitivity to asymmetry. We
study a construction of homology of digraphs due to Grigor'yan, Lin, Muranov
and Yau, and extend this construction to the persistent framework. The result,
which we call persistent path homology, can provide information about the
digraph structure of a directed network at varying resolutions. Moreover, this
method encodes a rich level of detail about the asymmetric structure of the
input directed network. We test our method on both simulated and real-world
directed networks and conjecture some of its characteristics.

利用 Grigor'yan, Lin, Muranov 和 Yau 构造的有向图同调研究持久化范畴中的非对称性，提出了持久路径同调，并在模拟和真实的有向网络上验证了其特征。