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.

翻译：合成数据集在深度学习领域正被视为详尽标注真实数据的一种宝贵替代方案。公式驱动监督学习便是此类合成数据生成方法之一，它能够通过公式驱动的方法（如分形或轮廓）提供无限数量的完美标注数据。FDSL避免了人工劳动、隐私问题及其他伦理顾虑等常见缺陷。在本研究中，我们采用三维迭代函数系统生成三维分形，用于动作识别模型的预训练。这些分形经过时序变换形成视频，作为动作识别下游任务的预训练数据集。我们发现标准的分形生成方法速度缓慢且会产生退化的三维分形。因此，我们系统性地探索了替代的分形生成方法，发现限制性过强的方法虽然能生成视觉美观的分形，却会损害下游任务性能。我们提出了一种新颖的定向智能过滤方法，以同时解决生成速度与分形多样性的问题。该方法实现了约100倍的采样速度提升，并在下游任务性能上优于其他三维分形过滤方法。