LLM-Guided Synthetic Image Generation for DML Retrieval
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Solution Overview
Problem
Deep Metric Learning (DML) models for image retrieval suffer from limited generalizability due to scarcity of diverse data, leading to poor clean data performance and vulnerability to adversarial attacks, primarily because they are prone to overfitting when trained on limited datasets.
Innovation Solution
The RobustRetrieVAL framework automates synthetic data generation using Large Vision And Language models to address class-and domain-imbalances in datasets, enhancing model generalization by generating targeted synthetic data through a multi-modal approach that includes an image-to-text model, data insight generator, augmentation protocol selector, text-to-image model, and outlier removal and diversity control module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If DML models are trained on limited datasets, then training time and data requirements are reduced, but generalizability and robustness against adversarial attacks deteriorate
Solution Approach 1:
The patent employs text-to-image models to generate synthetic image copies that replicate the visual characteristics and semantic content of real images. These synthetic copies serve as augmented training data, enabling the model to learn from diverse variations without requiring additional real-world data collection, thereby improving generalizability while maintaining limited data requirements
Solution Approach 2:
The patent introduces text prompts and language models as intermediary elements between real images and synthetic image generation. This intermediary process enables controlled generation of diverse image variations by translating semantic descriptions into visual content, allowing the system to bridge the gap between limited real data and the need for diverse training examples
2Quantity of substance
If DML models are trained on limited datasets, then data collection and processing costs are reduced, but susceptibility to overfitting increases
Solution Approach 1:
The patent implements dynamic data augmentation by generating synthetic images on-the-fly during training using text-to-image models. This dynamic approach creates continuously varying training examples that adapt to the model's learning progress, preventing the model from memorizing static training patterns and thereby reducing overfitting while maintaining efficient use of limited real data
Solution Approach 2:
The patent varies multiple parameters in synthetic image generation including text prompt descriptions, styling options, and generation conditions. By changing these parameters to create diverse synthetic variations, the system enriches the training distribution without requiring more real data, effectively combating overfitting through increased parameter diversity
3Reliability
If synthetic data is generated to enhance diversity, then model generalizability improves, but system complexity and computational resources increase
Solution Approach 1:
The patent employs a universal text-to-image generation system that can produce diverse synthetic data across multiple domains and categories through a single model architecture. This multi-functional approach eliminates the need for separate data generation systems for different image types, reducing overall system complexity while maintaining the ability to generate diverse training data for improving model generalizability
4Reliability
If synthetic data is generated to enhance diversity, then representation of under-represented classes improves, but generation time and computational cost increase
Solution Approach 1:
The patent performs preliminary analysis of class distribution in the training data to identify under-represented categories before synthetic data generation. Based on this preliminary assessment, the system prioritizes generating synthetic examples for specific under-represented classes, enabling targeted data augmentation that efficiently improves class balance without uniformly generating excessive synthetic data across all categories
Data Source
AI summary
A method comprising generating image descriptions of images in an original training set of images; determining, using at least one LLM, at least one domain and/or class which is under-represented in the original training set; generating, using a second LLM and based on the determination of the at least one domain and/or class, at least one instruction for a third LLM to generate at least one text prompt; generating, using the third LLM and based on the at least one instruction, the at least one text prompt for a text-to-image model; generating, using the text-to-image model and based on the at least one text prompt, at least one synthetic image; and generating an enhanced training set of images for use in training an image processing machine learning, ML, model, the enhanced training set of images comprising the original training set of images and the at least one synthetic image.