Diffusion Noise Features for Training-Free Image Label Configuration
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Solution Overview
Problem
The challenge of adapting newly updated training data to deep learning models is inefficient due to the sporadic nature of to-be-added training data, leading to prolonged training processes that affect image processing efficiency in configuration.
Innovation Solution
An image processing method utilizing a diffusion model to calculate noise differences between reference and query libraries, enabling label configuration without training, by merging estimated noise features based on image labels and determining target labels through feature similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If both existing training data and to-be-added training data are inputted into the deep learning model for training, then the model can adapt to new data, but the training process takes long time
Solution Approach 1:
The patent pre-calculates and stores noise features for all reference images in the reference library before query operations. This preliminary action allows the system to quickly retrieve and compare pre-computed noise features during query operations, avoiding the need for real-time training or complex computations when new data needs to be adapted.
Solution Approach 2:
The patent uses noise features as a compact representation or copy of the essential characteristics of reference images. Instead of storing and processing full reference images or performing complete training operations, the system works with compressed noise feature vectors that capture the essential information needed for adaptation and comparison.
2Adaptability or versatility
If to-be-added training data is processed as sporadic samples, then data accumulation can occur, but model adaptation cannot be completed quickly
Solution Approach 1:
The patent transforms the problem from image space to noise feature space by changing the parameter representation. Instead of working directly with image data, the system converts images to noise features through the diffusion model, enabling efficient comparison and adaptation operations in the transformed feature space.
Solution Approach 2:
The patent creates noise feature copies of reference images that can be rapidly compared and combined. These feature copies enable quick adaptation operations without requiring access to or processing of the original large-sized image data.
3Reliability
If traditional training methods are used to adapt new data, then complete model adaptation can be achieved, but image processing efficiency in training data configuration is affected
Solution Approach 1:
The patent extracts the essential adaptation information into noise features, separating the critical characteristics from the full image data. This extraction allows the system to work with condensed representations that retain the necessary information for adaptation while eliminating redundant data that would slow down processing.
Solution Approach 2:
The patent pre-computes noise features for the reference library in advance, performing the computationally intensive transformation before query operations. This preliminary computation stores the essential adaptation information in an easily accessible and computationally efficient format.
Data Source
AI summary
In an image processing method, a reference library and a query library are obtained; a reference image in the reference library and a prompt are inputted into a diffusion model to obtain estimated noise; the estimated noise is merged to obtain a reference noise feature; a plurality of query noise features corresponding to a query image are determined; and a target label corresponding to the query image is determined based on feature similarities between the plurality of query noise features and the reference noise features.