Meta-Sim: Learning to Generate Synthetic Datasets
Name
1904.11621.pdf
Description
Submitted version
Size
8.54 MB
Format
Unknown
Checksum (MD5)
7e5490fe2ef66484e619e7e20fe748e6
Author(s)
Kar, Amlan
Prakash, Aayush
Liu, Ming-Yu
Cameracci, Eric
Yuan, Justin
Rusiniak, Matt
Acuna, David
Torralba, Antonio
Fidler, Sanja
Date Issued
February 2020
Journal
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
Institute of Electrical and Electronics Engineers (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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iccv.2019.00465
