VAE-GAN Domain Adaptation for Synthetic Image Generation
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Solution Overview
Problem
Current methods for training deep neural networks (DNNs) in vehicles to predict the location of moving objects in various environmental conditions are inefficient due to the need for large numbers of real-world training images, which are expensive and time-consuming to acquire, and photorealistic rendering programs struggle to accurately simulate diverse domains like rain or snow.
Innovation Solution
The use of a variational auto encoder-generative adversarial network (VAE-GAN) to adapt synthetic images from one domain to another, such as changing weather and lighting conditions, to generate a large dataset of domain-adapted synthetic images for training DNNs, allowing for robust object recognition in real-world scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If photorealistic rendering programs are used to simulate diverse environmental conditions, then the visual realism of synthetic images is improved, but the accuracy of simulating diverse domains like rain or snow deteriorates
Solution Approach 1:
The patent uses domain adaptation to copy visual characteristics from real images of specific domains (rain, snow, fog) and apply them to synthetic images. Instead of attempting to generate realistic weather effects from scratch, the system transfers domain-specific visual patterns from real-world examples to synthetic scenes, achieving both visual realism and domain accuracy simultaneously
Solution Approach 2:
The patent transforms images between different domains by modifying parameters such as weather conditions, lighting, and environmental factors. The domain adaptation process changes image parameters to match target domain characteristics while preserving the underlying scene structure, enabling accurate simulation of diverse environmental conditions
2Reliability
If large numbers of real-world training images are acquired for training deep neural networks, then the training data quality is improved, but the time and cost required for data acquisition increases
Solution Approach 1:
The patent generates synthetic training images that copy the essential visual and semantic characteristics of real-world images. By creating photorealistic synthetic scenes with accurate object representations, the system produces high-quality training data without the time-consuming process of capturing real-world images, while maintaining the statistical properties needed for effective DNN training
Solution Approach 2:
The patent performs domain adaptation and synthetic image generation in advance to create comprehensive training datasets. By pre-generating diverse synthetic images covering multiple domains and conditions, the system prepares high-quality training data before the actual training process, eliminating the need for time-consuming real-world data collection during deployment
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a synthetic image and corresponding ground truth and generate a plurality of domain adapted synthetic images by processing the synthetic image with a variational auto encoder-generative adversarial network (VAE-GAN), wherein the VAE-GAN is trained to adapt the synthetic image from a first domain to a second domain. The instructions can include further instructions to train a deep neural network (DNN) based on the domain adapted synthetic images and the corresponding ground truth and process images with the trained deep neural network to determine objects.