Synthetic Data for Deep Learning (1909.11512v1)

Abstract: Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, simulation environments for robotics, applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more); we also survey the work on improving synthetic data development and alternative ways to produce it such as GANs. Second, we discuss in detail the synthetic-to-real domain adaptation problem that inevitably arises in applications of synthetic data, including synthetic-to-real refinement with GAN-based models and domain adaptation at the feature/model level without explicit data transformations. Third, we turn to privacy-related applications of synthetic data and review the work on generating synthetic datasets with differential privacy guarantees. We conclude by highlighting the most promising directions for further work in synthetic data studies.

Synthetic Data for Deep Learning: A Comprehensive Survey

Synthetic data has become an integral component in the training and evaluation of deep learning models, particularly within the field of computer vision. This comprehensive survey by Nikolenko delves deeply into the multifaceted applications of synthetic data, its challenges, and future directions. The paper systematically examines various areas where synthetic data has made substantial impacts and highlights the ongoing developments in generating and utilizing synthetic datasets.

Applications and Challenges

Synthetic data's most prominent applications lie within computer vision tasks, including optical flow estimation, object detection, semantic segmentation, and scene understanding. Its utilization extends to training models for complex tasks such as autonomous driving and indoor navigation within simulated environments. The paper highlights the benefits of synthetic data's vast scalability and perfect labeling, which are essential in training data-hungry deep learning models.

The survey further elaborates on how synthetic data addresses the challenge of insufficient real-world data, which either lacks in scale or is costly to obtain due to manual labeling requirements. For instance, in semantic segmentation, synthetic data can provide pixel-perfect labels, which are labor-intensive to acquire manually.

However, the transition from synthetic to real-world applications—known as the domain gap—remains a significant challenge. Models trained on synthetic data often require domain adaptation techniques to function effectively in real-world settings. The paper discusses methods like GAN-based refiners and domain adaptation strategies that mitigate this gap by making synthetic data appear more realistic or by adapting feature-level representations for improved real-world performance.

Strong Numerical Results and Domain-Specific Insights

The paper provides compelling numerical evidence on synthetic data's efficacy. For example, it cites cases where training on synthetic datasets with strategic domain randomization leads to robust and generalizable models that rival or surpass those trained on significantly larger sets of real data. It also discusses domain-specific insights, such as how certain background classes in segmentation tasks benefit more from synthetic data due to texture realism.

Implications and Future Directions

Practically, synthetic data expands the horizon for deploying deep learning models in areas with scarce labeled data, such as medical imaging and autonomous vehicle training in varied conditions. Theoretically, it challenges the boundaries of data augmentation and simulation fidelity. The survey posits that future developments in synthetic data should focus on procedural generation, improving the feedback loop for data refinement, and enhancing domain adaptation via additional modalities such as depth and semantic information.

Furthermore, the integration of differential privacy within synthetic data generation ensures privacy-preserving model training, thus broadening its applicability in sensitive domains like healthcare and finance.

Speculations and Emerging Technologies

Looking forward, the survey speculates that advancements in procedural generation and adaptive domain randomization could significantly streamline synthetic data creation, reducing dependencies on hand-crafted scenes and models. Additionally, more sophisticated integration of synthetic data with domain-specific knowledge, particularly in robotics and interactive systems, may lead to more versatile and intelligent systems.

In summary, this survey underscores the transformative potential of synthetic data in deep learning, elucidating its current impact and setting the stage for future innovations in AI. As synthetic data continues to evolve, it promises to play a pivotal role in overcoming data limitations and propelling AI capabilities forward.