This paper, for the very first time, introduces human sketches to the
landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as
a ``human-centred'' data form, represents a natural interface to study
explainability. We focus on cultivating sketch-specific explainability designs.
This starts by identifying strokes as a unique building block that offers a
degree of flexibility in object construction and manipulation impossible in
photos. Following this, we design a simple explainability-friendly sketch
encoder that accommodates the intrinsic properties of strokes: shape, location,
and order. We then move on to define the first ever XAI task for sketch, that
of stroke location inversion SLI. Just as we have heat maps for photos, and
correlation matrices for text, SLI offers an explainability angle to sketch in
terms of asking a network how well it can recover stroke locations of an unseen
sketch. We offer qualitative results for readers to interpret as snapshots of
the SLI process in the paper, and as GIFs on the project page. A minor but
interesting note is that thanks to its sketch-specific design, our sketch
encoder also yields the best sketch recognition accuracy to date while having
the smallest number of parameters. The code is available at
https://sketchxai.github.io.

该文章首次将人类草图引入 XAI（可解释人工智能）领域，提出以草图为数据形式的可解释人工智能设计，通过识别笔画的形状、位置和顺序等内在属性，定义了首个用于草图的 XAI 任务：笔画位置倒置。研究结果不仅提供了草图解释的角度，同时还展示了该特殊设计的草图编码器最佳的草图识别精度和最小的参数数量。

SketchXAI：人类草图解释的初探

SketchXAI: A First Look at Explainability for Human Sketches

We show how to teach machines to paint like human painters, who can use a
small number of strokes to create fantastic paintings. By employing a neural
renderer in model-based Deep Reinforcement Learning (DRL), our agents learn to
determine the position and color of each stroke and make long-term plans to
decompose texture-rich images into strokes. Experiments demonstrate that
excellent visual effects can be achieved using hundreds of strokes. The
training process does not require the experience of human painters or stroke
tracking data. The code is available at
this https URL

通过在基于模型的深度强化学习中应用神经渲染器，我们展示了如何教授机器像人类画家一样用少量笔画来创作精美的油画。实验表明，不需要画家的经验或笔画跟踪数据，可以使用数百个笔画取得出色的视觉效果。