Black-Box Classifier Compression Using KL Divergence Thresholds
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Solution Overview
Problem
Existing data compression techniques for machine learning models, particularly black box classifiers, face challenges in determining an optimal compression quality that maintains classifier accuracy without requiring deep understanding of the model or multiple iterations.
Innovation Solution
The method involves determining a global KL divergence threshold and establishing a relationship between compression quality and KL divergences, allowing for the determination of an optimal compression quality that ensures adequate classifier accuracy, even for black box models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If lossy compression techniques are used to reduce bandwidth consumption, then bandwidth usage is optimized, but classifier accuracy deteriorates
Solution Approach 1:
The patent changes the parameter of compression quality by determining an optimal value that balances bandwidth reduction and accuracy maintenance. It uses KL divergence thresholding to identify the point where compression begins to significantly degrade classifier performance, allowing systematic optimization of the compression quality parameter.
Solution Approach 2:
The patent introduces KL divergence as an intermediary metric to mediate between compression quality and classifier accuracy. By using KL divergence thresholding, it creates an intermediate measurement that predicts accuracy degradation before it occurs, enabling indirect control of the accuracy-compression tradeoff.
2Ease of operation
If empirical techniques are used to determine compression quality, then compression settings can be adjusted, but the process requires deep understanding of the classification model and multiple iterations
Solution Approach 1:
The patent performs preliminary action by pre-determining the optimal compression quality using KL divergence thresholding before actual compression operations. This upfront analysis eliminates the need for multiple iterative trials, as the optimal compression setting is calculated in advance based on the relationship between KL divergence and classifier accuracy.
Solution Approach 2:
The patent replaces the mechanical trial-and-error process with a mathematical substitution approach. Instead of empirically testing multiple compression qualities, it substitutes the iterative process with a direct calculation using KL divergence metrics, which mathematically determines the optimal compression quality without requiring physical iterations.
3Reliability
If high compression quality is used to maintain classifier accuracy, then classification performance is preserved, but bandwidth savings are reduced
Solution Approach 1:
The patent optimizes the compression quality parameter by identifying the precise point where further compression would cause unacceptable accuracy degradation. It uses KL divergence thresholding to determine the maximum compression level that maintains performance, thereby optimizing the parameter to achieve the best possible bandwidth savings without sacrificing accuracy.
Solution Approach 2:
The patent uses KL divergence as an intermediary to precisely measure the relationship between compression quality and classifier accuracy. This intermediary metric allows for fine-tuned optimization by identifying the exact compression quality threshold where accuracy maintenance and bandwidth savings are optimally balanced.
Data Source
AI summary
Optimizing lossy compression for classification models with unlabeled data is disclosed. In determining a compression quality, a global KL divergence threshold for input data is determined. If a divergence between the KL divergence of data and perturbed data is less than the global KL divergence threshold, a classifier will perform within a percentage of its original accuracy. A relationship between the compression quality {circumflex over (q)}nd the KL divergences of the compressed and decompressed data, after being processed by the classifier is determined. An optimal compression quality is determined based on the global KL divergence threshold and the relationship.