Good quality similarity metrics can significantly facilitate the performance
of many large-scale, real-world applications. Existing studies have proposed
various solutions to learn a Mahalanobis or bilinear metric in an online
fashion by either restricting distances between similar (dissimilar) pairs to
be smaller (larger) than a given lower (upper) bound or requiring similar
instances to be separated from dissimilar instances with a given margin.
However, these linear metrics learned by leveraging fixed bounds or margins may
not perform well in real-world applications, especially when data distributions
are complex. We aim to address the open challenge of "Online Adaptive Metric
Learning" (OAML) for learning adaptive metric functions on the fly. Unlike
traditional online metric learning methods, OAML is significantly more
challenging since the learned metric could be non-linear and the model has to
be self-adaptive as more instances are observed. In this paper, we present a
new online metric learning framework that attempts to tackle the challenge by
learning an ANN-based metric with adaptive model complexity from a stream of
constraints. In particular, we propose a novel Adaptive-Bound Triplet Loss
(ABTL) to effectively utilize the input constraints and present a novel
Adaptive Hedge Update (AHU) method for online updating the model parameters. We
empirically validate the effectiveness and efficacy of our framework on various
applications such as real-world image classification, facial verification, and
image retrieval.

本文提出了一种新的自适应度量学习框架，其通过基于 ANN 的度量学习来自适应地学习模型复杂度，并且通过自适应约束的流来进行学习，并通过 Adaptive-Bound Triplet Loss（ABTL）利用输入的约束，通过 Adaptive Hedge 更新（AHU）方法在线更新模型参数，我们在真实世界的图像分类，面部验证和图像检索等各种应用中验证了我们框架的有效性和功效。

自适应度量学习的研究

Towards Self-Adaptive Metric Learning On the Fly

We study few-shot learning in natural language domains. Compared to many
existing works that apply either metric-based or optimization-based
meta-learning to image domain with low inter-task variance, we consider a more
realistic setting, where tasks are diverse. However, it imposes tremendous
difficulties to existing state-of-the-art metric-based algorithms since a
single metric is insufficient to capture complex task variations in natural
language domain. To alleviate the problem, we propose an adaptive metric
learning approach that automatically determines the best weighted combination
from a set of metrics obtained from meta-training tasks for a newly seen
few-shot task. Extensive quantitative evaluations on real-world sentiment
analysis and dialog intent classification datasets demonstrate that the
proposed method performs favorably against state-of-the-art few shot learning
algorithms in terms of predictive accuracy. We make our code and data available
for further study.

提出了一种自适应度量学习方法，该方法能够从元训练任务中获得一组评估度量，并自动确定最佳加权组合，以捕捉自然语言领域中新的 few-shot 任务的复杂任务变化。在真实情感分析和对话意图分类数据集上进行了广泛的定量评估，结果表明所提出的方法在预测准确性方面表现优于现有的 few shot learning 算法。