Deep Metric Learning (DML) models rely on strong representations and
similarity-based measures with specific loss functions. Proxy-based losses have
shown great performance compared to pair-based losses in terms of convergence
speed. However, proxies that are assigned to different classes may end up being
closely located in the embedding space and hence having a hard time to
distinguish between positive and negative items. Alternatively, they may become
highly correlated and hence provide redundant information with the model. To
address these issues, we propose a novel approach that introduces Soft
Orthogonality (SO) constraint on proxies. The constraint ensures the proxies to
be as orthogonal as possible and hence control their positions in the embedding
space. Our approach leverages Data-Efficient Image Transformer (DeiT) as an
encoder to extract contextual features from images along with a DML objective.
The objective is made of the Proxy Anchor loss along with the SO
regularization. We evaluate our method on four public benchmarks for
category-level image retrieval and demonstrate its effectiveness with
comprehensive experimental results and ablation studies. Our evaluations
demonstrate the superiority of our proposed approach over state-of-the-art
methods by a significant margin.

本文提出了一种基于 Soft Orthogonality (简称 SO) 的新方法来解决深度度量学习 (DML) 中代理点出现的问题，并结合数据高效的图像变换器 (DeiT) 作为编码器提取图片的上下文特征，以数据集检索等实验结果表明该方法在超越现有技术的同时，还具有更高的可靠性和实用性

软正交代理的深度度量学习

Deep Metric Learning with Soft Orthogonal Proxies

One of the main purposes of deep metric learning is to construct an embedding
space that has well-generalized embeddings on both seen (training) classes and
unseen (test) classes. Most existing works have tried to achieve this using
different types of metric objectives and hard sample mining strategies with
given training data. However, learning with only the training data can be
overfitted to the seen classes, leading to the lack of generalization
capability on unseen classes. To address this problem, we propose a simple
regularizer called Proxy Synthesis that exploits synthetic classes for stronger
generalization in deep metric learning. The proposed method generates synthetic
embeddings and proxies that work as synthetic classes, and they mimic unseen
classes when computing proxy-based losses. Proxy Synthesis derives an embedding
space considering class relations and smooth decision boundaries for robustness
on unseen classes. Our method is applicable to any proxy-based losses,
including softmax and its variants. Extensive experiments on four famous
benchmarks in image retrieval tasks demonstrate that Proxy Synthesis
significantly boosts the performance of proxy-based losses and achieves
state-of-the-art performance.

本研究提出了一种名为 Proxy Synthesis 的正则化方法，旨在加强深度度量学习中的强泛化能力，尤其关注于构建同时适用于已知和未知类别的嵌入空间，其中包含了合成嵌入和代理的相关性及其对于合成类的模拟能力，并通过类关系和平滑决策边界，进一步实现对于未知类别的鲁棒性。实验证明该方法可以极大提升基于代理的模型的性能，并达到了最先进水平。

代理合成：使用合成类学习深度度量学习

Proxy Synthesis: Learning with Synthetic Classes for Deep Metric  Learning

Existing metric learning losses can be categorized into two classes:
pair-based and proxy-based losses. The former class can leverage fine-grained
semantic relations between data points, but slows convergence in general due to
its high training complexity. In contrast, the latter class enables fast and
reliable convergence, but cannot consider the rich data-to-data relations. This
paper presents a new proxy-based loss that takes advantages of both pair- and
proxy-based methods and overcomes their limitations. Thanks to the use of
proxies, our loss boosts the speed of convergence and is robust against noisy
labels and outliers. At the same time, it allows embedding vectors of data to
interact with each other in its gradients to exploit data-to-data relations.
Our method is evaluated on four public benchmarks, where a standard network
trained with our loss achieves state-of-the-art performance and most quickly
converges.

该研究提出了一种新的代理损失方法，它结合了基于对和代理的方法的优点并克服了它们的局限性，同时具有更快的收敛速度和对嘈杂标签和离群值的鲁棒性，能充分利用数据之间的关系，其中该方法在四个公共基准测试中表现出最先进的性能和最快的收敛速度。