Dataset Condensation via Learnable Codebook for Image Processing
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Solution Overview
Problem
Existing dataset condensation approaches face challenges in scalability and efficiency due to slow optimization processes and overfitting issues, particularly when dealing with large datasets and multiple classes, as they directly condense information into pixels, leading to a large number of learnable parameters and limited diversity in synthetic images.
Innovation Solution
The proposed solution uses a generative model with a learnable codebook to condense datasets, where synthetic images are generated based on class features, and the image generation process and codebook are updated to reduce differences between reference and target features, allowing for efficient scaling with varying classes and resolutions, and enhancing intra-class diversity and inter-class discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing dataset condensation approaches directly condense information into pixels, then the condensation process can be performed, but the optimization process becomes slow and the number of learnable parameters increases significantly
Solution Approach 1:
The patent segments the image representation into discrete codebook entries rather than treating all pixels as continuous learnable parameters. Each synthetic image is constructed by selecting and combining codebook entries, which dramatically reduces the number of learnable parameters while maintaining the ability to represent diverse image content through combinatorial arrangements of segmented code units.
Solution Approach 2:
The patent changes the parameter representation from continuous pixel values to discrete codebook indices. This parameter transformation reduces the optimization space from continuous high-dimensional pixel space to discrete code selection space, thereby reducing the number of learnable parameters and accelerating the optimization process while preserving essential image information.
2Loss of information
If existing dataset condensation approaches use a large number of learnable parameters, then more information can be captured, but the model is prone to overfitting and generalization performance decreases
Solution Approach 1:
The patent creates a codebook that serves as a compressed representation copying essential image features and structures. Instead of learning all pixel details directly, the model learns to select and combine codebook entries that capture the most important information, thereby retaining essential image content while reducing overfitting through the bottleneck effect of the codebook.
Solution Approach 2:
By segmenting the image into codebook entries, the patent creates a hierarchical representation where the codebook acts as an intermediate layer that captures essential features. This segmentation prevents direct memorization of all pixel details, reducing overfitting while preserving critical information through the structured codebook representation.
3Quantity of substance
If existing dataset condensation approaches compress large datasets into synthetic images, then data efficiency improves, but the diversity of synthetic images is limited
Solution Approach 1:
The patent merges multiple codebook entries to construct synthetic images, allowing diverse combinations of learned code units to generate varied synthetic images. This combining approach enables the model to create diverse images by differentially selecting and arranging codebook entries, thereby maintaining high diversity despite the compressed dataset size.
Solution Approach 2:
The patent introduces a combinatorial dimension by allowing synthetic images to be constructed from multiple codebook entries in various arrangements. This adds a new dimension of diversity through combinatorial possibilities, enabling the generation of diverse synthetic images from a limited set of codebook entries without requiring large dataset sizes.
4Adaptability or versatility
If existing dataset condensation approaches are scaled up to large datasets, then coverage improves, but the condensation process becomes computationally infeasible
Solution Approach 1:
The patent changes the optimization parameters from continuous pixel values to discrete codebook indices, which dramatically reduces the computational complexity of scaling to large datasets. This parameter transformation makes the condensation process computationally feasible for large-scale datasets by reducing the optimization space and enabling more efficient gradient-based optimization.
Solution Approach 2:
By segmenting the image representation into codebook entries, the patent reduces the computational burden of processing large datasets. The segmentation approach allows for more efficient computation during both training and inference, making it feasible to scale the condensation process to large datasets while maintaining acceptable computational complexity.
Data Source
AI summary
A method, apparatus, device, and medium for image processing is provided. The method includes generating, using an image generation process, a first set of synthetic images based on a first set of codes associated with the first image class in a codebook and based on a first class feature associated with a first image class; generating, using a feature extraction process, a first set of reference features based on the first set of synthetic images and generating a first set of target features based on a plurality of sets of training images belonging to the first image class in a training image set; and updating the image generation process and the codebook according to at least a first training objective to reduce a difference between each reference feature in the first set of reference features and a corresponding target feature in the first set of target features.