Neural Network Concept Extraction for Detection Failure Analysis
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Solution Overview
Problem
Deep neural networks used in object detection, particularly in autonomous driving, are opaque and difficult to debug, making them unsuitable for safety-critical applications due to their complex and non-transparent decision logic.
Innovation Solution
A method is introduced that involves a trained concept extraction entity coupled with intermediate layers of a convolutional neural network to provide semantic concept information, enabling the identification of concepts leading to detection failures and allowing for improved prediction transparency and automated monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for object detection, then detection performance is improved, but transparency and debuggability deteriorate
Solution Approach 1:
The patent introduces an intermediary explanation system that translates the internal representations of the deep neural network into human-understandable concepts. This intermediary layer includes a concept extractor that identifies semantic concepts from intermediate network layers and an explanation generator that creates human-readable explanations, thereby bridging the gap between complex network decisions and human comprehension without altering the original detection performance.
Solution Approach 2:
The patent segments the neural network analysis into distinct components: intermediate layer extraction, concept identification, concept weighting, and explanation generation. By dividing the complex decision-making process into these separable stages, the system makes each component analyzable and debuggable while maintaining the overall detection performance of the intact network.
2Measurement precision
If deep neural networks are used for object detection, then detection accuracy is improved, but safety certification capability deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the explanation system continuously monitors and analyzes the decision-making process of the neural network. By generating explanations for each detection decision and analyzing concept activations, the system provides feedback loops that enable verification of network behavior against safety requirements, facilitating certification while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary analysis of the neural network's decision logic before safety certification is required. By extracting concepts, analyzing their weights, and generating explanations in advance, the system prepares verification data and insights that can be used for safety certification, rather than attempting to certify the opaque network directly.
3Ease of operation
If concept extraction entity is coupled with intermediate layers, then prediction transparency is improved, but system complexity increases
Solution Approach 1:
The patent designs the concept extraction entity to serve multiple functions: extracting semantic concepts from intermediate layers, weighting these concepts by their importance, generating human-readable explanations, and providing insights for network improvement. This multi-functional approach consolidates what could be separate complex components into a unified system, reducing overall architectural complexity while maintaining transparency.
Data Source
Figure 1~2
AI summary
The invention relates to a computer-implemented method for finding the cause of detection failures of an artificial neural network, the artificial neural network being configured to detect objects in an image.