ALADIN-NST：通过神经风格迁移实现艺术风格的自监督解耦表示学习
ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer
AI总结：
本文提出 ALADIN-NST，利用 Neural Style Transfer 提供自监督学习信号，以学习与语义内容高度解耦的艺术风格表示，并在解耦指标和下游多模态任务上达到最先进水平。
摘要：
表示学习旨在以紧凑且具描述性的形式发现一个领域内的各个显著特征，从而相对于样本所属领域，强有力地识别给定样本的独特特征。视觉风格表示文献中的现有工作已尝试在训练期间显式地将风格与内容解耦，但二者之间的完全分离仍未充分实现。本文旨在学习一种视觉艺术风格表示，使其与图像所描绘的语义内容更强地解耦。我们使用 Neural Style Transfer（NST，神经风格迁移）来衡量并驱动学习信号，并在显式解耦指标上实现最先进的表示学习性能。我们表明，有力地解决风格与内容的解耦问题，会在风格特定指标上带来大幅提升，所编码的语义信息大幅减少，并在下游多模态应用中取得最先进的准确率。
英文摘要：
Representation learning aims to discover individual salient features of a domain in a compact and descriptive form that strongly identifies the unique characteristics of a given sample respective to its domain. Existing works in visual style representation literature have tried to disentangle style from content during training explicitly. A complete separation between these has yet to be fully achieved. Our paper aims to learn a representation of visual artistic style more strongly disentangled from the semantic content depicted in an image. We use Neural Style Transfer (NST) to measure and drive the learning signal and achieve state-of-the-art representation learning on explicitly disentangled metrics. We show that strongly addressing the disentanglement of style and content leads to large gains in style-specific metrics, encoding far less semantic information and achieving state-of-the-art accuracy in downstream multimodal applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
