Neural Network Data Augmentation via Background Modification
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Solution Overview
Problem
Neural networks face challenges in achieving robustness due to the limited quality of training datasets, particularly when training images do not adequately represent challenging scenarios, such as hand poses in diverse environments, making it difficult to construct a robust training dataset.
Innovation Solution
The implementation of data augmentation techniques, including background filtering, hue adjustment, and three-dimensional capture data rendering, to generate diverse training images by modifying backgrounds and selecting backgrounds based on inference scores for iterative inclusion in the training dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional training datasets are used, then training is simple and fast, but the neural network lacks robustness and fails on challenging scenarios
Solution Approach 1:
The system performs preliminary analysis of inference failures before training, identifying specific challenging scenarios (e.g., hand poses against certain backgrounds). Training images are then synthetically generated in advance to address these identified weaknesses, rather than relying on random or pre-collected datasets.
Solution Approach 2:
The system creates synthetic copies of training images by generating new images with modified backgrounds and conditions. These synthetic images replicate the challenging scenarios identified during inference analysis, allowing the network to learn from artificial but realistic examples without requiring physical re-capture of scenarios.
2Reliability
If diverse training images are manually collected, then robustness improves, but time and resources required increase significantly
Solution Approach 1:
Instead of manually collecting diverse images, the system synthesizes training images computationally by generating new images with modified backgrounds, lighting, and conditions. This copying approach creates unlimited diverse training data instantly without field collection efforts.
Solution Approach 2:
The system systematically varies parameters such as background types, lighting conditions, and pose configurations to generate diverse training images. By changing these parameters algorithmically, the system creates comprehensive training coverage without manual intervention for each scenario.
3Productivity
If training focuses on easy scenarios, then training converges quickly, but prediction accuracy deteriorates on challenging cases
Solution Approach 1:
The system performs preliminary inference analysis to identify specific failure modes before training begins. This allows targeted generation of challenging training examples that address actual weaknesses, ensuring training focuses on difficult cases that need improvement rather than easy cases that already work.
Solution Approach 2:
The system uses inference results as feedback to guide training data generation. By analyzing which scenarios cause failures during inference, the system continuously adapts the training dataset to address specific weaknesses, creating a feedback loop that improves accuracy on challenging cases while maintaining overall training efficiency.
Data Source
AI summary
In various examples, a background of an object may be modified to generate a training image. A segmentation mask may be generated and used to generate an object image that includes image data representing the object. The object image may be integrated into a different background and used for data augmentation in training a neural network. Data augmentation may also be performed using hue adjustment (e.g., of the object image) and/or rendering three-dimensional capture data that corresponds to the object from selected views. Inference scores may be analyzed to select a background for an image to be included in a training dataset. Backgrounds may be selected and training images may be added to a training dataset iteratively during training (e.g., between epochs). Additionally, early or late fusion nay be employed that uses object mask data to improve inferencing performed by a neural network trained using object mask data.