AI Playground：基于虚幻引擎的数据消融工具，用于深度学习
AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning
摘要：
机器学习需要数据，但获取和标记真实世界的数据是具有挑战性的，昂贵的，耗时的。更重要的是，几乎不可能在采集后改变真实数据（例如，改变房间的照明），使得很难测量数据的特定属性如何影响性能。在本文中，我们介绍了AI Playground（AIP），这是一个基于Unreal Engine的开源工具，用于生成和标记虚拟图像数据。使用AIP，在不同条件下捕获相同的图像是微不足道的（例如，保真度、照明等）并且具有不同的基本事实（例如，深度或表面法线值）。AIP很容易扩展，可以使用或不使用代码。为了验证我们提出的工具，我们生成了八个数据集，这些数据集在其他方面相同，但照明和保真度条件不同。然后，我们训练深度神经网络来预测（1）深度值，（2）表面法线，或（3）对象标签，并评估每个网络的数据集内和跨数据集性能。在其他见解中，我们验证了对不同设置的敏感性是依赖于问题的。我们证实了其他研究的发现，即分割模型对保真度非常敏感，但我们也发现它们对照明同样敏感。相比之下，深度和法线估计模型似乎对保真度或照明不太敏感，而对图像的结构更敏感。最后，我们在两个真实世界的数据集上测试了我们训练的深度估计网络，并获得了与仅在真实数据上训练相当的结果，证实了我们的虚拟环境对于真实世界的任务来说足够逼真。
英文摘要：
Machine learning requires data, but acquiring and labeling real-world data is challenging, expensive, and time-consuming. More importantly, it is nearly impossible to alter real data post-acquisition (e.g., change the illumination of a room), making it very difficult to measure how specific properties of the data affect performance. In this paper, we present AI Playground (AIP), an open-source, Unreal Engine-based tool for generating and labeling virtual image data. With AIP, it is trivial to capture the same image under different conditions (e.g., fidelity, lighting, etc.) and with different ground truths (e.g., depth or surface normal values). AIP is easily extendable and can be used with or without code. To validate our proposed tool, we generated eight datasets of otherwise identical but varying lighting and fidelity conditions. We then trained deep neural networks to predict (1) depth values, (2) surface normals, or (3) object labels and assessed each network's intra- and cross-dataset performance. Among other insights, we verified that sensitivity to different settings is problem-dependent. We confirmed the findings of other studies that segmentation models are very sensitive to fidelity, but we also found that they are just as sensitive to lighting. In contrast, depth and normal estimation models seem to be less sensitive to fidelity or lighting and more sensitive to the structure of the image. Finally, we tested our trained depth-estimation networks on two real-world datasets and obtained results comparable to training on real data alone, confirming that our virtual environments are realistic enough for real-world tasks.
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。
