Nowadays, Vision Transformer (ViT) is widely utilized in various computer
vision tasks, owing to its unique self-attention mechanism. However, the model
architecture of ViT is complex and often challenging to comprehend, leading to
a steep learning curve. ViT developers and users frequently encounter
difficulties in interpreting its inner workings. Therefore, a visualization
system is needed to assist ViT users in understanding its functionality. This
paper introduces EL-VIT, an interactive visual analytics system designed to
probe the Vision Transformer and facilitate a better understanding of its
operations. The system consists of four layers of visualization views. The
first three layers include model overview, knowledge background graph, and
model detail view. These three layers elucidate the operation process of ViT
from three perspectives: the overall model architecture, detailed explanation,
and mathematical operations, enabling users to understand the underlying
principles and the transition process between layers. The fourth interpretation
view helps ViT users and experts gain a deeper understanding by calculating the
cosine similarity between patches. Our two usage scenarios demonstrate the
effectiveness and usability of EL-VIT in helping ViT users understand the
working mechanism of ViT.

EL-VIT 是一种交互式可视化分析系统，可以帮助 ViT 用户理解 ViT 的工作机制，通过四个层次的可视化视图展示 ViT 的模型架构、操作过程和数学运算，以及计算图块之间的余弦相似度。

EL-VIT: 使用交互可视化研究视觉 Transformer

EL-VIT: Probing Vision Transformer with Interactive Visualization

Given thousands of equally accurate machine learning (ML) models, how can
users choose among them? A recent ML technique enables domain experts and data
scientists to generate a complete Rashomon set for sparse decision trees--a
huge set of almost-optimal interpretable ML models. To help ML practitioners
identify models with desirable properties from this Rashomon set, we develop
TimberTrek, the first interactive visualization system that summarizes
thousands of sparse decision trees at scale. Two usage scenarios highlight how
TimberTrek can empower users to easily explore, compare, and curate models that
align with their domain knowledge and values. Our open-source tool runs
directly in users' computational notebooks and web browsers, lowering the
barrier to creating more responsible ML models. TimberTrek is available at the
following public demo link: this https URL.

TimberTrek 是一款交互式可视化系统，可帮助机器学习从业人员在大规模的稀疏决策树集合中探索、比较和筛选满足其领域知识和价值观的模型，从而提高模型的解释性和责任性。

TimberTrek：交互可视化探索和整理稀疏决策树

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization

This extended abstract presents a visualization system, which is designed for
domain scientists to visually understand their deep learning model of
extracting multiple attributes in x-ray scattering images. The system focuses
on studying the model behaviors related to multiple structural attributes. It
allows users to explore the images in the feature space, the classification
output of different attributes, with respect to the actual attributes labelled
by domain scientists. Abundant interactions allow users to flexibly select
instance images, their clusters, and compare them visually in details. Two
preliminary case studies demonstrate its functionalities and usefulness.

介绍了一个可视化系统，供领域科学家使用，以便于视觉理解他们多属性提取的深度学习模型。该系统着重于研究与多种结构属性相关的模型行为，允许用户在特征空间中探索图像，并根据领域科学家标记的实际属性选择不同属性的分类输出。丰富的交互功能使用户可以灵活地选择实例图像和它们的聚类，并在细节上进行视觉比较。两个初步案例研究展示了该系统的功能和实用性。