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Meta-Sim: Learning to Generate Synthetic Datasets
Name
1904.11621.pdf
Description
Submitted version
Size
8.54 MB
Format
Adobe PDF
Checksum (MD5)
7e5490fe2ef66484e619e7e20fe748e6
Date Issued
October 2020
Journal
Proceedings of the IEEE International Conference on Computer Vision
Publisher
IEEE
Citation
2020. "Meta-Sim: Learning to Generate Synthetic Datasets." Proceedings of the IEEE International Conference on Computer Vision, 2019-October.
Version
Original manuscript
Abstract
© 2019 IEEE. Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task. We propose Meta-Sim, which learns a generative model of synthetic scenes, and obtain images as well as its corresponding ground-truth via a graphics engine. We parametrize our dataset generator with a neural network, which learns to modify attributes of scene graphs obtained from probabilistic scene grammars, so as to minimize the distribution gap between its rendered outputs and target data. If the real dataset comes with a small labeled validation set, we additionally aim to optimize a meta-objective, i.e. downstream task performance. Experiments show that the proposed method can greatly improve content generation quality over a human-engineered probabilistic scene grammar, both qualitatively and quantitatively as measured by performance on a downstream task.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/ICCV.2019.00465
