AutoSimulate: (Quickly) Learning Synthetic Data Generation (2008.08424v1)

Abstract: Simulation is increasingly being used for generating large labelled datasets in many machine learning problems. Recent methods have focused on adjusting simulator parameters with the goal of maximising accuracy on a validation task, usually relying on REINFORCE-like gradient estimators. However these approaches are very expensive as they treat the entire data generation, model training, and validation pipeline as a black-box and require multiple costly objective evaluations at each iteration. We propose an efficient alternative for optimal synthetic data generation, based on a novel differentiable approximation of the objective. This allows us to optimize the simulator, which may be non-differentiable, requiring only one objective evaluation at each iteration with a little overhead. We demonstrate on a state-of-the-art photorealistic renderer that the proposed method finds the optimal data distribution faster (up to $50\times$), with significantly reduced training data generation (up to $30\times$) and better accuracy ($+8.7\%$) on real-world test datasets than previous methods.

• The paper presents a differentiable bi-level optimization framework to efficiently tune simulator parameters for synthetic data generation.
• It employs a Newton step strategy with automatic differentiation to significantly reduce computation time compared to REINFORCE and Bayesian methods.
• Experiments on CLEVR and Arnold simulators demonstrate up to a 5x reduction in data generation overhead while preserving high segmentation accuracy and mAP.

Overview of "AutoSimulate: (Quickly) Learning Synthetic Data Generation"

The paper presents "AutoSimulate," a methodology aimed at efficiently generating synthetic data for machine learning models through a novel optimization framework. The traditional reliance on manually tuning simulator parameters or employing costly reinforcement learning techniques for data generation poses significant computational challenges. Addressing these issues, the authors offer a differentiable approximation for optimizing simulator parameters that ostensibly reduces the overhead of data generation. This paper proposes an advance in the automated creation of synthetic data, crucial for tasks where acquiring real-world labeled data is burdensome.

Methodological Insights

AutoSimulate tackles the synthetic data generation problem by reframing it as a bi-level optimization task. The inner loop deals with training a model on data generated by the simulator, while the outer loop optimizes simulator parameters based on the validation performance of the trained model. The innovative aspect of this work lies in leveraging a differentiable approximation that circumvents the non-differentiability of simulators. By doing so, the authors propose a framework that requires only a single objective evaluation per iteration, drastically reducing computational costs compared to methods employing REINFORCE-like gradient estimators.

The core proposition involves approximating the inner optimization using a Newton step strategy, which expedites convergence and maintains accuracy. The method exploits automatic differentiation to perform Hessian-vector products, thus enabling efficient scalability to large neural networks and complex simulator settings.

Empirical Evaluation

The authors conduct experiments using both a CLEVR simulator and Arnold renderer to validate the proposed approach. Within the CLEVR framework, AutoSimulate demonstrated a reduction in data generation by up to 5 times while maintaining segmentation accuracy comparable to baseline methods such as Bayesian optimization and REINFORCE. For real-world datasets, AutoSimulate optimized the Arnold simulator parameters effectively, achieving noteworthy improvements in object detection mean average precision (mAP) with a considerable reduction in data generation and computational time.

The results indicate that AutoSimulate outperforms existing methods in optimizing high-dimensional simulator parameters with significant reductions in training overheads. The experiments confirm the method's robustness across varying tasks, demonstrating its potential applicability in practical ML pipelines where synthetic data is a necessity.

Implications and Speculative Outlook

The introduction of AutoSimulate significantly impacts the practical efficiency of synthetic data generation. The reduction in computational resources required for iterative data generation and validation suggests that more complex machine learning models could be trained with better-customized datasets without the prohibitive costs traditionally associated with such approaches.

From a theoretical perspective, this work reinforces the viability of applying bi-level optimization strategies to non-differentiable simulators and opens avenues for further research into optimization frameworks that can accommodate even more intricate simulation environments. Looking ahead, such methodologies may also influence developments in areas like domain adaptation and incremental learning, where synthetic data plays a pivotal role.

In summary, "AutoSimulate: (Quickly) Learning Synthetic Data Generation" contributes significantly to advancing the techniques for generating high-quality synthetic data efficiently, ultimately enhancing the adaptability and performance of machine learning models across domains.