Interpreting Low-level Vision Models with Causal Effect Maps

• URL: http://arxiv.org/abs/2407.19789v2
• Date: Thu, 10 Oct 2024 03:56:53 GMT
• Title: Interpreting Low-level Vision Models with Causal Effect Maps
• Authors: Jinfan Hu, Jinjin Gu, Shiyao Yu, Fanghua Yu, Zheyuan Li, Zhiyuan You, Chaochao Lu, Chao Dong,
• Abstract summary: We introduce causality theory to interpret low-level vision models. We propose a model-/task-agnostic method called Causal Effect Map (CEM) CEM visualizes and quantify the input-output relationships on either positive or negative effects.
• Score: 25.07089157448049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have significantly improved the performance of low-level vision tasks but also increased the difficulty of interpretability. A deep understanding of deep models is beneficial for both network design and practical reliability. To take up this challenge, we introduce causality theory to interpret low-level vision models and propose a model-/task-agnostic method called Causal Effect Map (CEM). With CEM, we can visualize and quantify the input-output relationships on either positive or negative effects. After analyzing various low-level vision tasks with CEM, we have reached several interesting insights, such as: (1) Using more information of input images (e.g., larger receptive field) does NOT always yield positive outcomes. (2) Attempting to incorporate mechanisms with a global receptive field (e.g., channel attention) into image denoising may prove futile. (3) Integrating multiple tasks to train a general model could encourage the network to prioritize local information over global context. Based on the causal effect theory, the proposed diagnostic tool can refresh our common knowledge and bring a deeper understanding of low-level vision models. Codes are available at https://github.com/J-FHu/CEM.

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