Semantic-Aware Image Generation Training for Single-Sample Overfitting
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Solution Overview
Problem
Existing image generation models face overfitting issues when trained with a single sample image, leading to poor training effects due to a lack of sufficient sample images under the same category.
Innovation Solution
An image processing method that utilizes sample identifiers and category information to train the image generation model by extracting training representation information, generating noise images, predicting noise, and updating model parameters based on differences between marked and predicted noise, as well as training semantics, to enhance denoising processing and improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single sample image is used to train the image generation model, then the training process is simple and fast, but the model suffers from overfitting and poor training effect
Solution Approach 1:
The patent segments the training process into multiple stages: first training with a single sample image to establish basic features, then introducing category information and sample identifiers to refine and generalize the model. This segmentation allows the model to learn efficiently from limited data while preventing overfitting through structured progression.
Solution Approach 2:
The patent introduces category information and sample identifiers as intermediary elements between the single sample image and the final training objective. These intermediaries act as bridges that connect the specific sample to its broader category, enabling the model to generalize without requiring multiple diverse samples.
2Reliability
If multiple sample images under the same category are used to train the model, then the training effect is improved, but the data requirement increases and the training process becomes more complex
Solution Approach 1:
The patent creates virtual copies of the single sample image by generating variations based on category information and sample identifiers. Instead of requiring multiple distinct images, the system synthesizes multiple training instances from one image through semantic understanding and categorical generalization, effectively multiplying the training data without increasing physical data collection.
Solution Approach 2:
The patent changes the training parameters by introducing category labels and sample identifiers as additional training dimensions. This transforms the training from purely image-based to multi-parameter training, where the model learns both visual features and their categorical relationships, enabling better generalization from fewer images.
3Adaptability or versatility
If category information and sample identifiers are introduced during training, then the model's ability to generalize is improved, but the device complexity increases
Solution Approach 1:
The patent designs the training system to handle multiple functions: it processes single sample images, extracts category information, generates sample identifiers, and performs both image-based and semantic-based training. This multi-functional approach consolidates what would otherwise require separate systems into a unified framework, reducing overall complexity while enhancing versatility.
Data Source
AI summary
An image processing method, apparatus, and computer-readable storage medium for training image generation models through semantic-aware denoising. The method obtains sample data including a sample image, its category, and identifier set. Training representation information is extracted from the sample identifier to represent training semantics expressing sample features under the category. A noise image is generated by adding marked noise to the sample image encoding. Based on training representation information, noise in the noise image is predicted. Model parameters are updated using differences between marked and predicted noise and between training semantics of the sample identifier and category semantics. The trained model performs denoising on noise images using text description information including sample identifiers to generate diffusion images.