Abstract
Synthetic datasets are being recognized in the deep learning realm as a valuable alternative to exhaustively labeled real data. One such synthetic data generation method is Formula Driven Supervised Learning (FDSL), which can provide an infinite number of perfectly labeled data through a formula driven approach, such as fractals or contours. FDSL does not have common drawbacks like manual labor, privacy and other ethical concerns. In this work we generate 3D fractals using 3D Iterated Function Systems (IFS) for pre-training an action recognition model. The fractals are temporally transformed to form a video that is used as a pre-training dataset for downstream task of action recognition. We find that standard methods of generating fractals are slow and produce degenerate 3D fractals. Therefore, we systematically explore alternative ways of generating fractals and finds that overly-restrictive approaches, while generating aesthetically pleasing fractals, are detrimental for downstream task performance. We propose a novel method, Targeted Smart Filtering, to address both the generation speed and fractal diversity issue. The method reports roughly 100 times faster sampling speed and achieves superior downstream performance against other 3D fractal filtering methods.
Abstract (translated)
合成数据在深度学习领域中被认为是一种有价值的现实标记数据的替代方案。其中一种生成合成数据的方法是公式驱动监督学习（Formula Driven Supervised Learning，FDSL），它可以通过公式驱动的方式，如分形或轮廓，提供无限量的完美标注数据，而无需人工劳动，并且避免了隐私和其他伦理问题。在这项工作中，我们使用3D迭代函数系统（IFS）生成3D分形来预训练动作识别模型。这些分形在时间上变换形成视频，作为下游动作识别任务的预训练数据集。 我们发现传统的生成分形的方法速度慢，并且产生的三维分形质量较差。因此，我们系统地探索了替代方法以生成分形，结果发现过于严格的限制性方法虽然可以产生美观的分形图像，但对下游任务性能有害。为此，我们提出了一种新的方法——目标智能过滤法（Targeted Smart Filtering），旨在同时解决生成速度和分形多样性的问题。该方法报告称采样速度快了大约100倍，并且在与其他3D分形筛选方法相比时，在下游任务中表现出色。
URL
https://arxiv.org/abs/2602.11810
