Next-generation deep learning based on simulators and synthetic data.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Deep learning (DL) models typically require extensive labeled data, posing a significant bottleneck for development.
• Current DL training methods are often artificial, lacking the natural learning capabilities of biological systems.

Purpose of the Study:

• To highlight synthetic data as a solution to the supervised data access challenge in deep learning.
• To explore the potential of synthetic data to enable more natural learning paradigms.
• To discuss the role of synthetic data, simulators, and deep neural networks (DNNs) in understanding biological systems.

Main Methods:

• Leveraging progress in rendering pipelines, generative adversarial models, and fusion models for synthetic data creation.
• Utilizing domain adaptation techniques to bridge the statistical gap between synthetic and real-world data.
• Reviewing the strengths, opportunities, and challenges associated with synthetic data.

Main Results:

• Synthetic data generation is becoming increasingly accessible.
• Advancements in domain adaptation are improving the utility of synthetic data.
• Synthetic data shows promise for enabling continual, multimodal, and embodied learning.

Conclusions:

• Synthetic data offers a viable solution to the data bottleneck in deep learning.
• This artificial data approach may pave the way for more natural AI learning.
• Simulators and DNNs, in conjunction with synthetic data, can provide valuable insights into biological cognition and neural function.