What Sketch Explainability Really Means for Downstream Tasks
arxiv(2024)
摘要
In this paper, we explore the unique modality of sketch for explainability,
emphasising the profound impact of human strokes compared to conventional
pixel-oriented studies. Beyond explanations of network behavior, we discern the
genuine implications of explainability across diverse downstream sketch-related
tasks. We propose a lightweight and portable explainability solution – a
seamless plugin that integrates effortlessly with any pre-trained model,
eliminating the need for re-training. Demonstrating its adaptability, we
present four applications: highly studied retrieval and generation, and
completely novel assisted drawing and sketch adversarial attacks. The
centrepiece to our solution is a stroke-level attribution map that takes
different forms when linked with downstream tasks. By addressing the inherent
non-differentiability of rasterisation, we enable explanations at both coarse
stroke level (SLA) and partial stroke level (P-SLA), each with its advantages
for specific downstream tasks.
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