We present a novel self-taught framework for unsupervised metric learning,
which alternates between predicting class-equivalence relations between data
through a moving average of an embedding model and learning the model with the
predicted relations as pseudo labels. At the heart of our framework lies an
algorithm that investigates contexts of data on the embedding space to predict
their class-equivalence relations as pseudo labels. The algorithm enables
efficient end-to-end training since it demands no off-the-shelf module for
pseudo labeling. Also, the class-equivalence relations provide rich supervisory
signals for learning an embedding space. On standard benchmarks for metric
learning, it clearly outperforms existing unsupervised learning methods and
sometimes even beats supervised learning models using the same backbone
network. It is also applied to semi-supervised metric learning as a way of
exploiting additional unlabeled data, and achieves the state of the art by
boosting performance of supervised learning substantially.

本文提出了一种新的自学习框架用于无监督度量学习，该框架通过交替预测数据之间的类等价关系和利用预测的关系作为伪标签来学习模型。其中的算法能够有效地进行端到端的训练，因为它不需要伪标签的现成模块。通过在标准的度量学习基准上的表现，它明显优于现有的无监督学习方法，有时甚至比使用相同基础网络的监督学习模型更好。它也被应用于半监督的度量学习，并通过提高监督学习的性能，实现了现有最先进的表现。

无标签的自学度量学习

Self-Taught Metric Learning without Labels

Existing approaches for unsupervised metric learning focus on exploring
self-supervision information within the input image itself. We observe that,
when analyzing images, human eyes often compare images against each other
instead of examining images individually. In addition, they often pay attention
to certain keypoints, image regions, or objects which are discriminative
between image classes but highly consistent within classes. Even if the image
is being transformed, the attention pattern will be consistent. Motivated by
this observation, we develop a new approach to unsupervised deep metric
learning where the network is learned based on self-supervision information
across images instead of within one single image. To characterize the
consistent pattern of human attention during image comparisons, we introduce
the idea of transformed attention consistency. It assumes that visually similar
images, even undergoing different image transforms, should share the same
consistent visual attention map. This consistency leads to a pairwise
self-supervision loss, allowing us to learn a Siamese deep neural network to
encode and compare images against their transformed or matched pairs. To
further enhance the inter-class discriminative power of the feature generated
by this network, we adapt the concept of triplet loss from supervised metric
learning to our unsupervised case and introduce the contrastive clustering
loss. Our extensive experimental results on benchmark datasets demonstrate that
our proposed method outperforms current state-of-the-art methods for
unsupervised metric learning by a large margin.

该研究提出了一种基于变换后的注意力图的无监督深度度量学习方法，通过学习图像在比较中产生的视觉注意图的一致性，使用孪生深度神经网络对图像及其变化或匹配对进行编码和比较，并采用三元损失和对比聚类损失来增强生成特征的跨类别差别能力，实验结果表明该方法在无监督度量学习方面的表现超越了当前的最先进方法。