Region-Constrained Mask for Adversarial Example Interpretability
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Solution Overview
Problem
Conventional techniques for explaining machine learning model predictions fail to exclude specific regions of input data, leading to inaccurate and incomplete explanations, particularly in cases where a dominant feature overwhelms the optimization process.
Innovation Solution
The method involves generating a mask to disregard portions of the input data during backpropagation, iteratively modifying it using a loss function that encourages smooth, continuous regions of influence, and progressively growing the explanation mask to improve prediction accuracy and explanatory quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional explanation techniques are used to identify influential input regions, then the explanation process can be performed, but the explanation is incomplete or inaccurate when a dominant feature overwhelms the optimization process
Solution Approach 1:
The patent segments the input data by introducing a mask that divides the input into explained regions and excluded regions. This segmentation allows the explanation process to focus on specific areas while ignoring dominant but irrelevant features, thereby improving explanation accuracy without being overwhelmed by strong contributions from any single region.
Solution Approach 2:
The patent extracts and removes specific regions from the explanation process by applying a mask that sets certain areas to zero. This extraction mechanism allows the system to exclude dominant features that would otherwise overwhelm the optimization process, enabling more accurate explanations of truly influential regions.
2Productivity
If the optimization process focuses on minimizing loss with respect to input, then the explanation can be generated, but the process converges to only partial explanation when a small area has strong contribution
Solution Approach 1:
The patent performs preliminary action by pre-defining a mask before the optimization process begins. This mask pre-identifies regions to be excluded from explanation, preventing the optimization from being dominated by strong but irrelevant features. This preliminary structuring enables the subsequent optimization to converge to a more complete and accurate explanation.
Solution Approach 2:
The patent applies local quality by allowing different regions of the input to have different treatment through the mask. Explained regions are optimized normally while excluded regions are masked out. This local differentiation ensures that the optimization process allocates attention appropriately across different areas, improving both the completeness and accuracy of the explanation.
3Area of stationary object
If all input regions are considered in the explanation process, then comprehensive coverage is achieved, but the explanation accuracy decreases due to inclusion of irrelevant or dominant features
Solution Approach 1:
The patent introduces a mask as an intermediary element between the input data and the explanation process. This intermediary selectively transmits information from different regions, allowing comprehensive coverage of the input space while filtering out irrelevant or dominant features that would degrade explanation quality. The mask acts as a gatekeeper that controls which regions contribute to the final explanation.
Data Source
AI summary
Embodiments may exclude portions of input data in order to improve the accuracy and explanatory quality of the output of machine learning models by disregarding parts of the input during the optimization process by masking them during backpropagation. For example, in an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise receiving, at the computer system, input data and a machine learning model to generate a prediction based on the input data, generating, at the computer system, a mask indicating portions of the input data to be disregarded during backpropagation of the machine learning model, and modifying, at the computer system, the generated mask to improve the prediction of the machine learning model.