Summary of Genetic Learning For Designing Sim-to-real Data Augmentations, by Bram Vanherle et al.
Genetic Learning for Designing Sim-to-Real Data Augmentations
by Bram Vanherle, Nick Michiels, Frank Van Reeth
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the relationship between image augmentation techniques and their effectiveness in bridging the sim-to-real domain gap when training object detection models on synthetic data. The authors introduce two interpretable metrics that can be combined to predict the performance of different augmentation policies, which are then validated through extensive experiments. Moreover, GeneticAugment, a genetic programming method, is proposed to automatically design an optimal augmentation policy for a specific dataset without model training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper discusses how image augmentations help close the sim-to-real domain gap when training on synthetic data by widening the training data distribution and encouraging better generalization to other domains. It highlights the various possible augmentation policies that exist, each with different strength and probability settings. The authors then present two metrics that can be combined to predict which policy will work best for a specific sim-to-real setting. |
Keywords
» Artificial intelligence » Generalization » Object detection » Probability » Synthetic data
