Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)

• URL: http://arxiv.org/abs/2503.17416v1
• Date: Fri, 21 Mar 2025 01:12:57 GMT
• Title: Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)
• Authors: Boyue Caroline Hu, Divya Gopinath, Corina S. Pasareanu, Nina Narodytska, Ravi Mangal, Susmit Jha,
• Abstract summary: We show the utility of semantic heatmaps for fault localization in vision models.<n>We propose a lightweight runtime analysis to detect and filter-out defects at runtime.
• Score: 20.420310876464924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Debugging of Deep Neural Networks (DNNs), particularly vision models, is very challenging due to the complex and opaque decision-making processes in these networks. In this paper, we explore multi-modal Vision-Language Models (VLMs), such as CLIP, to automatically interpret the opaque representation space of vision models using natural language. This in turn, enables a semantic analysis of model behavior using human-understandable concepts, without requiring costly human annotations. Key to our approach is the notion of semantic heatmap, that succinctly captures the statistical properties of DNNs in terms of the concepts discovered with the VLM and that are computed off-line using a held-out data set. We show the utility of semantic heatmaps for fault localization -- an essential step in debugging -- in vision models. Our proposed technique helps localize the fault in the network (encoder vs head) and also highlights the responsible high-level concepts, by leveraging novel differential heatmaps, which summarize the semantic differences between the correct and incorrect behaviour of the analyzed DNN. We further propose a lightweight runtime analysis to detect and filter-out defects at runtime, thus improving the reliability of the analyzed DNNs. The runtime analysis works by measuring and comparing the similarity between the heatmap computed for a new (unseen) input and the heatmaps computed a-priori for correct vs incorrect DNN behavior. We consider two types of defects: misclassifications and vulnerabilities to adversarial attacks. We demonstrate the debugging and runtime analysis on a case study involving a complex ResNet-based classifier trained on the RIVAL10 dataset.

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