In this work we present a novel unsupervised framework for hard training
example mining. The only input to the method is a collection of images relevant
to the target application and a meaningful initial representation, provided
e.g. by pre-trained CNN. Positive examples are distant points on a single
manifold, while negative examples are nearby points on different manifolds.
Both types of examples are revealed by disagreements between Euclidean and
manifold similarities. The discovered examples can be used in training with any
discriminative loss. The method is applied to unsupervised fine-tuning of
pre-trained networks for fine-grained classification and particular object
retrieval. Our models are on par or are outperforming prior models that are
fully or partially supervised.

该研究提出了一种无监督框架，用于针对性地寻找训练样例。该方法仅需要一组与目标应用程序相关的图像和有效的初始表示，其中正样例是单个流形上的远点，而负样例是不同流形上的相邻点。这种方法可应用于预训练网络的无监督微调，用于细粒度分类和特定物体检索。与之前的完全或部分有监督的模型相比，我们的模型表现出相似或更好的性能。

流形挖掘：无标签度量学习

Mining on Manifolds: Metric Learning without Labels

Given a pre-trained CNN without any testing samples, this paper proposes a
simple yet effective method to diagnose feature representations of the CNN. We
aim to discover representation flaws caused by potential dataset bias. More
specifically, when the CNN is trained to estimate image attributes, we mine
latent relationships between representations of different attributes inside the
CNN. Then, we compare the mined attribute relationships with ground-truth
attribute relationships to discover the CNN's blind spots and failure modes due
to dataset bias. In fact, representation flaws caused by dataset bias cannot be
examined by conventional evaluation strategies based on testing images, because
testing images may also have a similar bias. Experiments have demonstrated the
effectiveness of our method.

本研究提出了一种简单而有效的方法来诊断 CNN 的特征表示，以发现由数据库偏差引起的表示缺陷和失败模式，并使用隐含属性关系和基础真相关系进行比较来挖掘潜在关联性。实验表明该方法的有效性。