NeuroInspect: Interpretable Neuron-based Debugging Framework through Class-conditional Visualizations

• URL: http://arxiv.org/abs/2310.07184v2
• Date: Tue, 17 Oct 2023 09:00:22 GMT
• Title: NeuroInspect: Interpretable Neuron-based Debugging Framework through Class-conditional Visualizations
• Authors: Yeong-Joon Ju, Ji-Hoon Park, and Seong-Whan Lee
• Abstract summary: We present NeuroInspect, an interpretable neuron-based debug framework for deep learning (DL) models. Our framework first pinpoints neurons responsible for mistakes in the network and then visualizes features embedded in the neurons to be human-interpretable. We validate our framework by addressing false correlations and improving inferences for classes with the worst performance in real-world settings.
• Score: 28.552283701883766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite deep learning (DL) has achieved remarkable progress in various domains, the DL models are still prone to making mistakes. This issue necessitates effective debugging tools for DL practitioners to interpret the decision-making process within the networks. However, existing debugging methods often demand extra data or adjustments to the decision process, limiting their applicability. To tackle this problem, we present NeuroInspect, an interpretable neuron-based debugging framework with three key stages: counterfactual explanations, feature visualizations, and false correlation mitigation. Our debugging framework first pinpoints neurons responsible for mistakes in the network and then visualizes features embedded in the neurons to be human-interpretable. To provide these explanations, we introduce CLIP-Illusion, a novel feature visualization method that generates images representing features conditioned on classes to examine the connection between neurons and the decision layer. We alleviate convoluted explanations of the conventional visualization approach by employing class information, thereby isolating mixed properties. This process offers more human-interpretable explanations for model errors without altering the trained network or requiring additional data. Furthermore, our framework mitigates false correlations learned from a dataset under a stochastic perspective, modifying decisions for the neurons considered as the main causes. We validate the effectiveness of our framework by addressing false correlations and improving inferences for classes with the worst performance in real-world settings. Moreover, we demonstrate that NeuroInspect helps debug the mistakes of DL models through evaluation for human understanding. The code is openly available at https://github.com/yeongjoonJu/NeuroInspect.

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