Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models

--Evaluating, explaining, and visualizing high-level concepts in generative models, such as variational autoencoders (V AEs), is challenging in part due to a lack of known prediction classes that are required to generate saliency maps in supervised learning. While saliency maps may help identify relevant features (e.g., pixels) in the input for classification tasks of deep neural networks, similar frameworks are understudied in unsupervised learning. Therefore, we introduce a new method of obtaining saliency maps for latent representations of known or novel high-level concepts, often called concept vectors in generative models. Concept scores, analogous to class scores in classification tasks, are defined as dot products between concept vectors and encoded input data, which can be readily used to compute the gradients. The resulting concept saliency maps are shown to highlight input features deemed important for high-level concepts. Our method is applied to the V AE's latent space of CelebA dataset in which known attributes such as "smiles" and "hats" are used to elucidate relevant facial features. Furthermore, our application to spatial transcriptomic (ST) data of a mouse olfactory bulb demonstrates the potential of latent representations of morphological layers and molecular features in advancing our understanding of complex biological systems. By extending the popular method of saliency maps to generative models, the proposed concept saliency maps help improve interpretability of latent variable models in deep learning. I NTRODUCTION A rapidly increasing amount of unlabeled data, such as images and molecular data, has prompted a rise of deep generative models, that can be trained without human supervision. By using a vast amount of unlabeled data, unsupervised learning models such as variational autoencoders (V AEs) [1], [2] extract low-dimensional latent spaces that compactly encode high-dimensional input data and potentially reveal hidden relationships.