Meta-Sim: Learning to Generate Synthetic Datasets (1904.11621v1)

Abstract: 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.

An Overview of Meta-Sim: Generating Synthetic Datasets for Improved Downstream Task Performance

The paper "Meta-Sim: Learning to Generate Synthetic Datasets" presents a methodology for generating synthetic datasets aimed at enhancing downstream task performance while minimizing the domain gap between synthetic and real-world data. This work addresses the critical challenge of data availability and labeling costs in machine learning, particularly when dealing with large labeled datasets necessary for high-performance models.

Core Contributions

The authors propose Meta-Sim, a framework designed to generate highly relevant synthetic datasets tailored to specific downstream tasks. Meta-Sim employs a generative model parameterized by a neural network, which modifies the attributes of scene graphs derived from probabilistic grammars. This modification is aimed at aligning the synthetic dataset distribution with the target real-world data distribution. Importantly, Meta-Sim takes into account a potential small real-world validation set to optimize the synthetic data for direct task performance.

Methodology

The paper presents a detailed architectural design and training pipeline for Meta-Sim:

• Scene Graphs and Probabilistic Grammars: Scene graphs, a structured representation of 3D worlds with hierarchical dependencies among scene elements, are modified using probabilistic grammars. Meta-Sim's neural network adjusts these graphs to better match the diversity and layout seen in real-world datasets.
• Distribution Matching and Task Optimization: Meta-Sim introduces a novel joint objective for training that includes distribution matching using Maximum Mean Discrepancy (MMD) and a meta-objective of downstream task performance. The latter leverages a task network trained on the synthetic data, with the performance optimizing the attributes of the scene graph.
• Empirical Validation: The framework was validated across toy datasets (MNIST-like synthesized data) and more complex scenarios like self-driving car datasets (simulated KITTI datasets). The experiments demonstrated significant improvements in task performance, indicating that the synthetic datasets generated by Meta-Sim closely resemble the target real-world distribution.

Implications and Future Directions

The implications of this work are significant for machine learning and artificial intelligence. Primarily, it offers a framework for reducing the cost and time associated with labeling datasets by automating the generation of synthetic datasets that are highly task-specific and domain-relevant. This is particularly valuable for commercial and industrial applications where diverse datasets are constantly needed to accommodate various practical tasks, such as autonomous driving, where real-world data availability is a bottleneck.

Looking forward, future research avenues include enhancing the flexibility of the probabilistic grammars to dynamically adapt to varying task requirements and integrating Meta-Sim with differentiable renderers, which may offer more refined adjustments to scene attributes. Additionally, further exploration into handling highly variable domains and multimodal distributions could expand the applicability of Meta-Sim to broader AI tasks.

In conclusion, Meta-Sim lays a robust foundation towards intelligently bridifying the gap between simulated and real-world data distributions, offering a scalable path for continuous improvement in model training pipelines.