CNN Inference Error Localization via Attention Map Segmentation
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Solution Overview
Problem
Current analysis techniques for image recognition processes using convolutional neural networks (CNNs) fail to specify the image sections causing incorrect inference with adequate precision.
Innovation Solution
An analysis apparatus that generates a refine image maximizing the correct label score, creates an attention degree map indicating regions of similar attention levels, and extracts specific pixel regions from this map to visualize the inference error, thereby improving precision in identifying incorrect inference causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analysis techniques (activation maximization, Grad-CAM, BP methods) are used to identify incorrect inference causes, then the analysis can be performed with existing methods, but the precision in specifying image sections causing incorrect inference is insufficient
Solution Approach 1:
The patent segments the image analysis process into multiple precise steps: generating attention maps through backpropagation, thresholding to identify critical regions, extracting specific image sections from those regions, and synthesizing error cause images. This segmentation allows precise localization of inference errors while maintaining systematic control over the analysis complexity.
Solution Approach 2:
The patent introduces attention maps as an intermediary element that bridges the input image and the error analysis. These attention maps, generated through backpropagation of the loss function, serve as a mediator to highlight critical regions, enabling precise error localization without directly modifying the complex neural network structure.
2Measurement precision
If the entire image is analyzed for inference errors, then comprehensive coverage is achieved, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent extracts only the critical image sections that contribute to inference errors, rather than analyzing the entire image. By thresholding attention maps and selecting regions exceeding the threshold, the method isolates error-causing portions, significantly reducing processing time while maintaining accurate error identification.
Solution Approach 2:
The patent applies local quality analysis by focusing computational resources on specific image regions with high attention values rather than uniformly processing the entire image. This localized approach concentrates analysis where it is most needed, improving efficiency without sacrificing comprehensive error detection.
3Measurement precision
If existing analysis methods are used, then the implementation is straightforward with available techniques, but the ability to accurately visualize and analyze inference errors is limited
Solution Approach 1:
The patent merges multiple existing techniques (backpropagation for attention maps, thresholding for region selection, and image synthesis) into a unified error analysis framework. This combination leverages the strengths of each individual method while achieving superior error visualization capability, balancing implementation feasibility with enhanced accuracy.
Data Source
AI summary
An analysis method executed by a computer includes acquiring a refine image that maximizes a score for inferring a correct label by an inferring process using a trained model, the refine image being generated from an input image used when an incorrect label is inferred; generating a map indicating a region of pixels having the same or similar level of attention degree related to inference in the inferring process, of a plurality of pixels in the generated refine image, based on a feature amount used in the inferring process; extracting an image corresponding to a pixel region whose level in the generated map is a predetermined level, from calculated images calculated based on the input image and the refine image; and generating an output image that specifies a portion related to an inference error in the inferring process, among the calculated images, based on image processing on the extracted image.