Image Recognition Learning Device Using Synthetic Data Generation
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Solution Overview
Problem
Current image recognition technologies face challenges in achieving high accuracy with a small number of learning images, as they require extensive labeling and collection of large datasets, which is time-consuming and costly.
Innovation Solution
An image recognition learning device and method that uses a combination of iterative learning with a first loss function to enhance class probability similarity and a second loss function to differentiate between actual and artificial images, allowing for training with a reduced number of labeled images by generating artificial images that mimic actual ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of learning images are used to train the image recognition device, then recognition accuracy is improved, but the time and cost for collecting and labeling images increases significantly
Solution Approach 1:
The patent generates artificial images that copy the essential features and characteristics of actual images. These synthetic images serve as substitutes for real images, allowing the training process to proceed without requiring extensive collection and manual labeling of physical images. The generated images maintain the statistical properties and visual patterns needed for effective recognition training.
Solution Approach 2:
The system performs self-labeling by automatically generating both the training images and their corresponding ground truth labels through the image generation model. This eliminates the need for manual annotation by human labelers, allowing the dataset to create and label its own training samples without external human intervention.
2Measurement precision
If a large number of learning images are collected and labeled, then recognition accuracy is improved, but the cost of image collection and labeling increases
Solution Approach 1:
The patent creates synthetic copies of images through computational generation rather than physical acquisition. This replaces expensive processes of hiring photographers, purchasing stock images, or commissioning custom photo shoots with a computational approach that has minimal marginal cost per additional training image.
Solution Approach 2:
The automated image generation and labeling system eliminates the need for human labor in image acquisition and annotation. By using algorithms to generate both images and their corresponding labels automatically, the system removes the primary cost drivers of manual image collection and labeling processes.
3Measurement precision
If the image recognition device is trained with more learning images, then recognition accuracy is improved, but the complexity of the training process increases
Solution Approach 1:
The patent simplifies the training data preparation by generating images with known ground truth directly through the generative model. This approach avoids the complex pipeline of image collection, manual annotation quality control, and dataset curation that would otherwise be required to create large-scale labeled training datasets.
Solution Approach 2:
The system automatically manages the entire training data lifecycle through self-generation and self-labeling. This unified approach consolidates multiple complex subprocesses into a single automated workflow, reducing the overall complexity of the training process despite enabling the use of large numbers of training images.
Data Source
AI summary
An image identification device can be trained to identify classes with high accuracy even in cases with a small number of learning images. Using a first loss function for outputting a value that is smaller the greater a similarity is between the belongingness probability of each class for the image output by the image identification device and a given teacher belongingness probability of the image, and a second loss function for, in a case in which the image input into the image identification device is an actual image, outputting a value that is smaller the smaller the estimated authenticity probability, which expresses how artificial the input image is, output by the image identification device is and for, in a case in which the image input into the image identification device is an artificial image, outputting a value that is smaller the greater the estimated authenticity probability output by the image identification device is, iterative learning of a parameter of the image identification device is executed to reduce the value of the first loss function and the value of the second loss function.