Synthetic Image Generation via Subspace Projection for Neural Network Training
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Solution Overview
Problem
Neural networks require large datasets for training, which can be impractical to collect for all scenarios, and simulated data may lack realism, leading to sub-optimal training performance.
Innovation Solution
A method that uses a real-world dataset to generate a subspace of feature vectors, projects simulated images onto this subspace to create synthetic images with realistic features, and trains the neural network with these images, improving training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If simulated images are used for neural network training, then training data availability is improved, but image realism deteriorates
Solution Approach 1:
The patent creates synthetic images by copying and transforming real-world images through a series of processing steps. Real images are transformed into feature vectors, projected onto a subspace, and converted back to synthetic images that replicate real-world appearance. This copying process enables unlimited training data generation while maintaining image realism through faithful reproduction of real image characteristics.
Solution Approach 2:
The patent changes parameters of real-world images by transforming them into feature vectors and projecting onto a subspace defined by basis vectors. This parameter transformation allows the system to generate synthetic images with varied characteristics while maintaining realism, effectively changing the state of images from raw pixel data to transformed feature representations that capture essential real-world properties.
2Manufacturing precision
If real-world datasets are used for training, then image realism is improved, but data collection feasibility deteriorates
Solution Approach 1:
Instead of collecting diverse real-world images directly, the patent creates synthetic copies of real images through systematic transformation and projection processes. This copying approach generates unlimited training data that replicates real-world appearance without requiring actual data collection from all possible scenarios, making the process feasible and scalable.
Solution Approach 2:
The patent performs preliminary transformation of real images into feature vectors and creation of the subspace basis before generating synthetic images. This preliminary action prepares the essential building blocks (basis vectors) that can then be used to generate unlimited synthetic images, effectively pre-computing the transformation framework needed for data generation.
3Measurement precision
If more training data is collected, then training accuracy is improved, but time consumption increases
Solution Approach 1:
The patent generates synthetic images by copying and transforming existing real images through automated computational processes. This copying mechanism produces unlimited training data instantaneously without requiring time-consuming physical data collection, thereby achieving high training accuracy without proportional increases in time consumption.
Solution Approach 2:
The patent replaces the mechanical process of physical data collection with computational transformations. Instead of manually capturing images from various scenarios, the system uses automated image processing, feature extraction, and projection algorithms to generate synthetic training data, substituting mechanical data gathering with efficient computational operations.
Data Source
AI summary
A computer includes a processor and a memory, the memory storing instructions executable by the processor to apply a transform function to a plurality of images from a real-world dataset to generate a plurality of feature vectors, to apply a subspace generation algorithm to generate basis vectors of a subspace, and to project a simulated image onto the subspace to generate a realistic synthetic image.