Personalized ML Fine-Tuning With Frozen Conditioning Signals
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Solution Overview
Problem
Existing personalized machine learning models suffer from overfitting, particularly when fine-tuned for a small number of users with similar image features, leading to a lack of generalization and generation of images that replicate training dataset properties.
Innovation Solution
Implementing a system that uses frozen conditioning models to generate conditioning images and signals that disentangle structural information from identity information, allowing the model to be fine-tuned with these signals to prevent overfitting, using models like ControlNet and T2I Adapter to process depth, edge, and pose information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the model is fine-tuned for a small number of users with similar image features, then the model achieves personalized generation capability, but the model suffers from overfitting and lacks generalization
Solution Approach 1:
The patent segments the image information into two distinct components: structural information (captured by frozen conditioning models like ControlNet and T2I Adapter) and identity information (captured by the fine-tuned machine learning model). This segmentation allows the model to learn personalized identity features without overfitting to specific structural patterns in the training data, thereby maintaining generalization ability while achieving personalized generation.
Solution Approach 2:
The patent introduces frozen conditioning models as intermediary components that process structural information separately from the main fine-tuned model. These intermediaries (ControlNet for pose/structure, T2I Adapter for depth) act as mediators that preserve structural consistency while allowing the fine-tuned model to focus on learning user-specific identity characteristics, thus preventing overfitting to the entire image dataset.
2Loss of information
If the model learns both structural and identity information together, then the model captures complete image features, but the model overfits to training dataset properties
Solution Approach 1:
The patent divides the image feature learning process into two separate streams: one for structural information and one for identity information. The frozen conditioning models handle structural features (pose, depth, geometry) while the fine-tuned model handles identity features (user-specific characteristics). This segmentation ensures that neither component overfits to the other's patterns, allowing complete feature capture without overfitting.
Solution Approach 2:
The patent applies different learning strategies to different parts of the image information. Structural information is processed with frozen conditioning models that maintain fixed learned representations, while identity information is processed with adaptable fine-tuned models. This local differentiation in quality and flexibility allows the system to capture complete features while preventing overfitting in the structural component.
3Loss of time
If the model is fine-tuned with limited training data, then the model achieves fast adaptation to users, but the model generates images that replicate training dataset properties
Solution Approach 1:
The patent extracts structural information from the training images and processes it separately through frozen conditioning models before it reaches the fine-tuned model. By taking out the structural component and handling it independently, the fine-tuned model only learns from user-specific identity features in the limited training data, preventing it from replicating general structural properties of the training dataset while still achieving fast adaptation.
Solution Approach 2:
The patent performs preliminary processing of structural information using pre-trained frozen conditioning models before the fine-tuned model processes the images. This preliminary action of extracting and freezing structural representations allows the subsequent fine-tuning to focus exclusively on identity features, enabling fast adaptation with limited data while avoiding replication of training dataset structural properties.
Data Source
AI summary
The present disclosure describes techniques for implementing overfitting reduction in a personalized machine learning model. At least one conditioning image is generated based on an image. The image comprises identity information of a user and structural information. At least one conditioning signal is generated based on the at least one conditioning image by at least one frozen conditioning model. The at least one conditioning signal indicates the structural information of the input image without the identity information. The personalized machine learning model corresponding to the user is fine-tuned based on the at least one conditioning signal. The personalized machine learning model is fine-tuned to disentangle the structural information from the identity information.