Deep Neural Network Training with Synthetic Data and GANs
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Solution Overview
Problem
Current deep neural networks require extensive computational resources and large datasets for training, particularly in image recognition tasks, due to their complexity and the need for extensive data processing, which can be time-consuming and costly.
Innovation Solution
The use of synthetic data and generative adversarial networks (GANs) to augment deep neural networks, allowing for the integration of context information and reducing the need for extensive real data collection and labeling, by processing synthetic labeled images and real unlabeled images to improve training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are trained using traditional methods with real data, then the model achieves accurate learning, but the training process requires extensive computational resources and large datasets which is time-consuming and costly
Solution Approach 1:
The patent applies preliminary action by pre-processing real images to extract contextual information and generating synthetic training data in advance. The system pre-computes feature maps, depth information, and semantic labels for synthetic objects, which are then integrated with real image backgrounds. This pre-preparation of training data reduces the need for extensive real data collection and labeling during the actual training process, thereby reducing training time while maintaining learning accuracy.
Solution Approach 2:
The patent uses copying by creating synthetic copies of real-world objects and scenes through 3D modeling and rendering. Instead of using only authentic real images which require expensive collection and annotation, the system generates synthetic replicas that preserve essential visual characteristics and contextual relationships. These synthetic copies serve as training data, significantly reducing the time and resources needed for data collection while maintaining sufficient learning accuracy.
2Measurement precision
If deep neural networks are trained using traditional methods with real data, then the model achieves accurate learning, but extensive real data collection and labeling is required which is costly
Solution Approach 1:
The patent applies copying by generating synthetic training data that replicates the essential characteristics of real-world images. The system creates 3D models of objects, renders them in various contexts, and extracts feature information that mirrors real image data. This synthetic copying eliminates the need for expensive manual annotation of real images while preserving the visual and contextual information necessary for accurate learning.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically generate its own training data without requiring external data collection efforts. The contextual information extraction and synthetic data generation processes are automated, allowing the system to create its own training datasets programmatically. This self-generated data approach eliminates the need for costly human annotators and data collection expeditions.
3Adaptability or versatility
If deep neural networks use more layers and nodes to increase capacity, then the network can process more complex tasks, but the computational complexity and data requirements increase significantly
Solution Approach 1:
The patent applies local quality by focusing computational resources on extracting and processing specific contextual information from images rather than processing entire images through deep networks. The system identifies and extracts key features such as depth maps, semantic segments, and object boundaries, processing only the relevant local information. This selective processing reduces the overall computational burden while maintaining the network's ability to handle complex tasks.
Solution Approach 2:
The patent uses segmentation by dividing the image processing task into distinct components: background extraction, object detection, feature map generation, and contextual information extraction. Each component is processed separately and then integrated, allowing the system to manage computational complexity through modular processing while maintaining high network capacity for complex tasks.
Data Source
AI summary
Methods and systems for advanced and augmented training of deep neural networks (DNNs) using synthetic data and innovative generative networks. A method includes training a DNN using synthetic data, training a plurality of DNNs using context data, associating features of the DNNs trained using context data with features of the DNN trained with synthetic data, and generating an augmented DNN using the associated features.