Lightweighted Sparse Autoencoder based on Explanable Contribution

As deep learning models become heavier, developing lightweight models with the least performance degradation is paramount. In this paper, we propose an algorithm, SHAP-SAE (SHapley Additive exPlanations based Sparse AutoEncoder), that can explicitly measure the contribution of units and links and selectively activate only important units and links, leading to a lightweight sparse autoencoder. This allows us to explain how and why the sparse autoencoder is structured. We show that the SHAP-SAE outperforms other algorithms including a dense autoencoder. It is also confirmed that the SHAP-SAE is robust against the harsh sparsity of the autoencoder, as it shows remarkably limited performance degradation even with high sparsity levels.