Multi-view representation learning aims to derive robust representations that
are both view-consistent and view-specific from diverse data sources. This
paper presents an in-depth analysis of existing approaches in this domain,
highlighting a commonly overlooked aspect: the redundancy between
view-consistent and view-specific representations. To this end, we propose an
innovative framework for multi-view representation learning, which incorporates
a technique we term 'distilled disentangling'. Our method introduces the
concept of masked cross-view prediction, enabling the extraction of compact,
high-quality view-consistent representations from various sources without
incurring extra computational overhead. Additionally, we develop a distilled
disentangling module that efficiently filters out consistency-related
information from multi-view representations, resulting in purer view-specific
representations. This approach significantly reduces redundancy between
view-consistent and view-specific representations, enhancing the overall
efficiency of the learning process. Our empirical evaluations reveal that
higher mask ratios substantially improve the quality of view-consistent
representations. Moreover, we find that reducing the dimensionality of
view-consistent representations relative to that of view-specific
representations further refines the quality of the combined representations.
Our code is accessible at: this https URL

多视角表示学习中的重叠与纯化问题得到了深入分析，提出了一种创新的多视角表示学习框架，结合了一种名为 “distilled disentangling” 的技术，通过引入遮蔽交叉视角预测的概念，实现了从多种数据源中提取紧凑、高质量的视角一致的表示，进一步减少了与视角特定表示的冗余，提升了学习过程的整体效率。

通过蒸馏解耦重新思考多视角表示学习

Rethinking Multi-view Representation Learning via Distilled  Disentangling

Image search engines enable the retrieval of images relevant to a query
image. In this work, we consider the setting where a query for similar images
is derived from a collection of images. For visual search, the similarity
measurements may be made along multiple axes, or views, such as style and
color. We assume access to a set of feature extractors, each of which computes
representations for a specific view. Our objective is to design a retrieval
algorithm that effectively combines similarities computed over representations
from multiple views. To this end, we propose a self-supervised learning method
for extracting disentangled view-specific representations for images such that
the inter-view overlap is minimized. We show how this allows us to compute the
intent of a collection as a distribution over views. We show how effective
retrieval can be performed by prioritizing candidate expansion images that
match the intent of a query collection. Finally, we present a new querying
mechanism for image search enabled by composing multiple collections and
perform retrieval under this setting using the techniques presented in this
paper.

本文介绍了一种自我监督学习的方法，用于提取图像的特征向量，并将其组合用于图像搜索引擎的检索算法中。这种方法能有效地减少特征重叠，提高查询的精确性。