FAPI-Net: A lightweight interpretable network based on feature augmentation and prototype interpretation

With the increasing application of deep neural networks, their performance requirements in various fields are increasing. Deep neural network models with higher performance generally have a high number of parameters and computation (FLOPs, Floating Point Operations), and have the black-box characteristic. This hinders the deployment of deep neural network models on low-power platforms, as well as sustainable development in high-risk decision-making fields. However, there is little work to ensure the interpretability of the model in the research on the lightweight of the deep neural network model. This paper proposed FAPI-Net (feature augmentation and prototype interpretation), a lightweight interpretable network. It combined feature augmentation convolution blocks and the prototype dictionary interpretability (PDI) module. The feature augmentation convolution block is composed of lightweight feature-map augmentation (FA) modules and a residual connection stack. The FA module could effectively reduce network parameters and computation without losing network accuracy. The PDI module can realize the visualization of model classification reasoning. FAPI-Net is designed regarding MobileNetV3's structure, and our experiments show that the FAPI-Net is more effective than MobileNetV3 and other advanced lightweight CNNs. Params and FLOPs on the ILSVRC2012 dataset are 2 and 20% lower than that on MobileNetV3, respectively, and FAPI-Net with a trainable PDI module has almost no loss of accuracy compared with baseline models. In addition, the ablation experiment on the CIFAR-10 dataset proved the effectiveness of the FA module used in FAPI-Net. The decision reasoning visualization experiments show that FAPI-Net could make the classification decision process of specific test images transparent.