Affiliation:
1. Fraunhofer Institute for Physical Measurement Techniques IPM, 79110 Freiburg, Germany
2. Faculty of Digital Media, Furtwangen University, 78120 Furtwangen, Germany
Abstract
Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we introduce a novel approach that applies Auto-ML methods to the process of generating synthetic datasets for training machine learning models. Our approach addresses the problem that generating synthetic datasets requires a complex data generator, and that developing and tuning a data generator for a specific scenario is a time-consuming and expensive task. Being able to reuse this data generator for multiple purposes would greatly reduce the effort and cost, once the process of tuning it to the specific domains of each task is automated. To demonstrate the potential of this idea, we have implemented a point cloud generator for simple scenes. The scenes from this generator can be used to train a neural network to semantically segment cars from the background. The simple composition of the scene allows us to reuse the generator for several different semantic segmentation tasks. The models trained on the datasets with the optimized domain parameters easily outperform a model without such optimizations, while the optimization effort is minimal due to our Auto-ML approach. Although the development of such complex data generators requires considerable effort, we believe that using Auto-ML for dataset creation has the potential to speed up the development of machine learning applications in domains where high-quality labeled data is difficult to obtain.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference28 articles.
1. Next-generation deep learning based on simulators and synthetic data;Torralba;Trends Cogn. Sci.,2022
2. (2024, January 11). Website of Unreal Engine. Available online: https://www.unrealengine.com/en-US/unreal-engine-5.
3. Zhou, Q.Y., Park, J., and Koltun, V. (2018). Open3D: A Modern Library for 3D Data Processing. arXiv, Available online: http://arxiv.org/abs/1801.09847.
4. The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo;Laupheimer;ISPRS Open J. Photogramm. Remote. Sens.,2021
5. Shermeyer, J., Hossler, T., Van Etten, A., Hogan, D., Lewis, R., and Kim, D. (2021, January 5–9). Rareplanes: Synthetic data takes flight. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Virtual.