Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models

TL;DR

This paper introduces concept saliency maps for visualizing relevant features in the latent space of deep generative models like VAEs, enhancing interpretability in both image and biological data analysis.

Contribution

It proposes a novel method to generate saliency maps for high-level concepts in unsupervised generative models, extending interpretability tools beyond classification tasks.

Findings

Concept saliency maps highlight important input features for high-level concepts.

Application to CelebA dataset elucidates facial attribute features.

Application to spatial transcriptomics data demonstrates biological interpretability.

Abstract

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