Summary of Towards Reducing Data Acquisition and Labeling For Defect Detection Using Simulated Data, by Lukas Malte Kemeter et al.
Towards Reducing Data Acquisition and Labeling for Defect Detection using Simulated Data
by Lukas Malte Kemeter, Rasmus Hvingelby, Paulina Sierak, Tobias Schön, Bishwajit Gosswam
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper investigates the potential of synthetic data to reduce the annotation costs associated with machine learning and computer vision applications. Synthetic data can be generated at a lower cost, making it an appealing substitute for real-world data. However, relying solely on synthetic data often leads to domain shifts between the simulated and real-world data, which hinders model performance. The authors propose various strategies to address this issue in object detection tasks, particularly in detecting defects in X-ray scans of aluminium wheels. They compare different approaches using both simulated and real-world X-ray images and find that the sim-2-real domain adaptation approach is more cost-efficient than a fully supervised oracle when given a fixed number of annotated samples. Training on a mix of synthetic and unlabeled real-world data also achieves comparable or better detection results at a lower cost. The authors emphasize the importance of research into the cost-efficiency of different training strategies to optimize budget allocation in applied machine learning projects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you’re trying to teach a computer to recognize defects in X-ray scans of car wheels. Annotating these images takes time and money, but what if we could create fake images that are similar enough to real ones? That’s the idea behind synthetic data. The authors of this paper explore how well a computer can learn from both real and fake images to detect defects. They found that by mixing together some real and fake training data, they got better results than using only one type or the other. This could save time and money in the long run. |
Keywords
» Artificial intelligence » Domain adaptation » Machine learning » Object detection » Supervised » Synthetic data
