Deep Metric Learning (DML) aims to learn representation spaces on which
semantic relations can simply be expressed through predefined distance metrics.
Best performing approaches commonly leverage class proxies as sample stand-ins
for better convergence and generalization. However, these proxy-methods solely
optimize for sample-proxy distances. Given the inherent non-bijectiveness of
used distance functions, this can induce locally isotropic sample
distributions, leading to crucial semantic context being missed due to
difficulties resolving local structures and intraclass relations between
samples. To alleviate this problem, we propose non-isotropy regularization
($\mathbb{NIR}$) for proxy-based Deep Metric Learning. By leveraging
Normalizing Flows, we enforce unique translatability of samples from their
respective class proxies. This allows us to explicitly induce a non-isotropic
distribution of samples around a proxy to optimize for. In doing so, we equip
proxy-based objectives to better learn local structures. Extensive experiments
highlight consistent generalization benefits of $\mathbb{NIR}$ while achieving
competitive and state-of-the-art performance on the standard benchmarks
CUB200-2011, Cars196 and Stanford Online Products. In addition, we find the
superior convergence properties of proxy-based methods to still be retained or
even improved, making $\mathbb{NIR}$ very attractive for practical usage. Code
available at this https URL

使用非同构正则化和标准化流方法改进基于代理的测量学习，在代理周围引入非同构分布的样本，从而更好地学习本地结构和优化语境，同时获得比现有方法更好的泛化表现和竞争性结果。