Evidential Interpretation Approach for Deep Neural Networks in High-Frequency Electromagnetic Wave Processing

Despite the widespread adoption of high-frequency electromagnetic wave (HF-EMW) processing, deep neural networks (DNNs) remain primarily black boxes. Interpreting the semantics behind the high-dimensional representations of a DNN is quite crucial for getting insights into the network. This study has proposed an evidential representation fusion approach that interprets the high-dimensional representations of a DNN as HF-EMW semantics, such as time- and frequency-domain signal features and their physical interpretation. In this approach, an evidential discrete model based on Dempster–Shafer theory (DST) converts a subset of DNN representations to mass function reasoning on a class set, indicating whether the subset contains HF-EMW semantics information. An interpretable continuous DST-based model maps the subset into HF-EMW semantics via representation fusion. Finally, the two DST-based models are extended to interpret the learning processes of high-dimensional DNN representations. Experiments on the two datasets with 2680 and 4000 groups of HF-EMWs demonstrate that the approach can find and interpret representation subsets as HF-EMW semantics, achieving an absolute fractional output change of 39.84% with an 10% removed elements in most important features. The interpretations can be applied for visual learning evaluation, semantic-guided reinforcement learning with an improvement of 4.23% on classification accuracy, and even HF-EMW full-waveform inversion.