Dimensionality reduction algorithms are often used to visualise
high-dimensional data. Previously, studies have used prior information to
enhance or suppress expected patterns in projections. In this paper, we adapt
such techniques for domain knowledge guided interactive exploration. Inspired
by Mapper and STAD, we present three types of lens functions for UMAP, a
state-of-the-art dimensionality reduction algorithm. Lens functions enable
analysts to adapt projections to their questions, revealing otherwise hidden
patterns. They filter the modelled connectivity to explore the interaction
between manually selected features and the data's structure, creating
configurable perspectives each potentially revealing new insights. The
effectiveness of the lens functions is demonstrated in two use cases and their
computational cost is analysed in a synthetic benchmark. Our implementation is
available in an open-source Python package:
this https URL

基于 Mapper 和 STAD 的灵感，本文针对 UMAP 提出了三种类型的镜头函数，用于领域知识引导的交互式探索，以实现将高维数据投影到二维空间并显示隐藏模式的目的。通过使用镜头函数，分析人员可以根据问题调整投影，揭示不可见的模式，并通过筛选连接性来探索手动选择的特征与数据结构之间的相互作用，从而创造可能揭示新见解的可配置视角。本文在两个使用案例中展示了镜头函数的有效性，并在合成基准测试中分析了它们的计算成本。

利用领域知识探索 UMAP 投影的镜头函数

Lens functions for exploring UMAP Projections with Domain Knowledge

In volume rendering, transfer functions are used to classify structures of
interest, and to assign optical properties such as color and opacity. They are
commonly defined as 1D or 2D functions that map simple features to these
optical properties. As the process of designing a transfer function is
typically tedious and unintuitive, several approaches have been proposed for
their interactive specification. In this paper, we present a novel method to
define transfer functions for volume rendering by leveraging the feature
extraction capabilities of self-supervised pre-trained vision transformers. To
design a transfer function, users simply select the structures of interest in a
slice viewer, and our method automatically selects similar structures based on
the high-level features extracted by the neural network. Contrary to previous
learning-based transfer function approaches, our method does not require
training of models and allows for quick inference, enabling an interactive
exploration of the volume data. Our approach reduces the amount of necessary
annotations by interactively informing the user about the current
classification, so they can focus on annotating the structures of interest that
still require annotation. In practice, this allows users to design transfer
functions within seconds, instead of minutes. We compare our method to existing
learning-based approaches in terms of annotation and compute time, as well as
with respect to segmentation accuracy. Our accompanying video showcases the
interactivity and effectiveness of our method.

使用自监督预训练视觉转换器的特征提取能力，我们提出了一种用于体绘制的传递函数定义方法。用户只需在切片查看器中选择感兴趣的结构，我们的方法将基于神经网络提取的高级特征自动选择类似结构。与之前的基于学习的传递函数方法不同，我们的方法不需要模型训练，并且可以进行快速推理，从而实现对体数据的交互式探索。通过与现有的基于学习的方法在注释和计算时间以及分割准确度方面的比较，我们展示了我们方法的交互性和有效性。

借助自监督视觉变换器进行神经转换函数设计

Leveraging Self-Supervised Vision Transformers for Neural Transfer  Function Design

In this paper, we present DendroMap, a novel approach to interactively
exploring large-scale image datasets for machine learning (ML). ML
practitioners often explore image datasets by generating a grid of images or
projecting high-dimensional representations of images into 2-D using
dimensionality reduction techniques (e.g., t-SNE). However, neither approach
effectively scales to large datasets because images are ineffectively organized
and interactions are insufficiently supported. To address these challenges, we
develop DendroMap by adapting Treemaps, a well-known visualization technique.
DendroMap effectively organizes images by extracting hierarchical cluster
structures from high-dimensional representations of images. It enables users to
make sense of the overall distributions of datasets and interactively zoom into
specific areas of interests at multiple levels of abstraction. Our case studies
with widely-used image datasets for deep learning demonstrate that users can
discover insights about datasets and trained models by examining the diversity
of images, identifying underperforming subgroups, and analyzing classification
errors. We conducted a user study that evaluates the effectiveness of DendroMap
in grouping and searching tasks by comparing it with a gridified version of
t-SNE and found that participants preferred DendroMap. DendroMap is available
at this https URL.

本文提出了 DendroMap 这一新方法，可以有效地组织和交互式地探索大规模图像数据集，通过提取高维图像的层次聚类结构，用户可以在不同的抽象层级上对数据集进行总体分布的检查和兴趣区域的交互式缩放以及透视。研究结果表明，DendroMap 可以帮助用户发现数据集和训练模型方面的见解，其效果优于 t-SNE 格子化版本，DendroMap 已经在网站上提供。