  Evaluating, explaining, and visualizing high-level concepts in generative
models, such as variational autoencoders (VAEs), 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 VAE'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.
  Codes to reproduce and to implement concept saliency maps:
https://github.com/lenbrocki/concept-saliency-maps
