Synthetic Image Training for Data-Scarce Classification Models
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Solution Overview
Problem
The high cost and time-consuming nature of manually collecting large-scale labeled datasets for training deep learning models in computer vision, coupled with data privacy and usage rights concerns, hinders effective model performance, particularly in data-scarce settings.
Innovation Solution
Generate synthetic images using text-to-image generation models and associate them with training labels based on text prompts, enabling model training without additional manual labeling, and utilize these synthetic data for pre-training or fine-tuning in data-scarce scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and labeling is used, then training data quality can be ensured, but the process becomes time-consuming and costly
Solution Approach 1:
The patent uses text-to-image generation models to create synthetic images that copy the essential characteristics and visual features of real images. These synthetic images serve as substitutes for manually collected and labeled real images, maintaining training quality while eliminating the time-consuming manual data collection and labeling process
Solution Approach 2:
The system employs automated text-to-image generation models that can autonomously create synthetic training images with associated labels without human intervention. The model generates images based on text prompts and automatically creates corresponding labels, enabling self-service data preparation that eliminates manual labor while maintaining data quality
2Quantity of substance
If manual data collection and labeling is used, then comprehensive training datasets can be obtained, but the cost and time requirements increase significantly
Solution Approach 1:
The patent leverages text-to-image generation models to mass-produce synthetic images that replicate the visual characteristics needed for training. This copying approach enables rapid generation of large-scale training datasets without the linear time and cost increases associated with manual data collection and labeling
Solution Approach 2:
The system changes the parameter of data source from manual real-world collection to automated synthetic generation. By adjusting the text prompts and generation parameters, the model can efficiently produce diverse training images at scale, dramatically improving data preparation productivity while maintaining comprehensive dataset coverage
3Reliability
If real-world data is used for training, then model performance can be improved, but data privacy and usage rights concerns arise
Solution Approach 1:
The patent creates synthetic copies of real-world images through text-to-image generation models. These synthetic images preserve the visual characteristics and patterns needed for training reliable models while eliminating privacy and usage rights issues by not using actual real-world images that may contain sensitive information or copyright restrictions
Solution Approach 2:
The text-to-image generation model acts as an intermediary between the need for realistic training data and the constraints of data privacy and usage rights. It translates text descriptions into synthetic images that mediate the training process, providing the necessary visual patterns without directly using protected real-world data
Data Source
AI summary
In model training based on synthetic data, synthetic images are generated by providing respective text prompts into a text-to-image generation model. Respective training labels associated with the synthetic images are also generated based on the used text prompts. A target model, which is configured to perform an image classification task, is trained based at least in part on the synthetic images and the associated training labels. Through this solution, a large scale of synthetic images can be automatically obtained and applicable for training a model for image classification, to improve the model performance with data-scare setting or in the case of model pre-training where the training data amount matters.