Neural Measurement Data Generator With Verifiable Likelihood
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Solution Overview
Problem
The manual labeling of training images for image classifiers is expensive, making it costly to obtain a sufficient quantity of labeled training images for training automated vehicles and robots.
Innovation Solution
A generator is used to convert input vectors from a latent space to realistic measurement data using a neural network that maps input vectors to distribution parameters, allowing for the generation of synthetic training images with a well-defined likelihood, enabling efficient production of labeled training images without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training images is used, then the quality of training data is ensured, but the cost and time required increase significantly
Solution Approach 1:
The patent uses image synthesis to create virtual copies of real-world scenes that replicate the visual characteristics and object relationships of actual images. These synthetic images serve as substitutes for manually labeled real images, maintaining training quality while eliminating the time-consuming manual labeling process. The generator creates photorealistic synthetic images that preserve the semantic information needed for training.
Solution Approach 2:
The patent replaces the mechanical process of manual labeling with an automated neural network-based generation system. Instead of human annotators mechanically going through images and assigning labels, the system uses a generator trained on real images to automatically produce synthetic labeled images, substituting human cognitive labor with computational processes.
2Measurement precision
If manual labeling of training images is used, then the accuracy of object detection is improved, but the cost of obtaining training data increases
Solution Approach 1:
The patent creates synthetic copies of real images that maintain the essential visual and semantic properties needed for accurate object detection. These synthetic images replicate object appearances, spatial relationships, and scene contexts, providing labeled training data at a fraction of the cost of manual annotation while preserving the accuracy needed for detection algorithms.
Solution Approach 2:
The system uses existing real images to train a generator that then creates synthetic images for training purposes. This self-service approach leverages the available real data to produce additional training material without requiring external manual labeling resources, making the process cost-effective and scalable.
3Device complexity
If synthetic training images are generated directly mapping input vectors to records, then the process is simplified, but the likelihood distribution of generated data becomes undefined
Solution Approach 1:
The patent introduces a random distribution as an intermediary between the input latent vector and the final generated record. Instead of directly mapping z to x, the system maps z to distribution parameters that characterize a random distribution, from which samples are then drawn. This intermediary layer provides a well-defined likelihood p(x|z) while maintaining the generative capability.
Solution Approach 2:
The patent changes the output of the neural network from direct record values to distribution parameters (mean, covariance, or other parameters characterizing a random distribution). This parameter transformation enables the system to represent uncertainty and variability in the generated data, providing a complete probabilistic description rather than a single deterministic output.
Data Source
AI summary
A generator for converting an input vector from a latent space to one or more records x of measurement data that is realistic with respect to a given application domain. The generator includes: a trained neural network that is configured to map the input vector to a set of distribution parameters that characterize a random distribution of realistic measurement data, where this random distribution is configured such that given said set of distribution parameters and at least one source of randomness, samples of realistic measurement data may be obtained; and a sampling module including a random or pseudo-random number generator as a source of randomness and configured to sample the realistic measurement data from the random distribution.