Enforcing Sparsity on Latent Space for Robust and Explainable Representations

Recently, dense latent variable models have shown promising results, but their distributed and potentially redundant codes make them less interpretable and less robust to noise. On the other hand, sparse representations are more parsimonious, providing better explainability and noise robustness, but it is difficult to enforce sparsity due to the complexity and computational cost involved. In this paper, we propose a novel unsupervised learning approach to enforce sparsity on the latent space for the generator model, utilizing a gradually sparsified spike and slab distribution as our prior. Our model is composed of a top-down generator network that maps the latent variable to the observations. We use maximum likelihood sampling to infer latent variables in the generator's posterior direction, and spike and slab regularization in the inference stage can induce sparsity by pushing non-informative latent dimensions toward zero. Our experiments show that the learned sparse latent representations preserve the majority of the information, and our model can learn disentangled semantics, increase the explainability of the latent codes, and enhance the robustness of the classification and denoising tasks.