Unsupervised discovery of Interpretable Visual Concepts

TL;DR

This paper introduces two novel methods, MAGE and Ms-IV, to enhance the interpretability of CNNs by discovering and visualizing semantic concepts, outperforming existing xAI techniques in localization and faithfulness.

Contribution

The paper presents MAGE and Ms-IV, new techniques for unsupervised discovery and visualization of interpretable visual concepts in CNNs, improving global interpretability.

Findings

Ms-IV achieves higher localization accuracy.

The combined approach helps detect biases in networks.

Qualitative analysis shows human agreement with visualized concepts.

Abstract

Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation. In this paper, we propose two methods, Maximum Activation Groups Extraction (MAGE) and Multiscale Interpretable Visualization (Ms-IV), to explain the model's decision, enhancing global interpretability. MAGE finds, for a given CNN, combinations of features which, globally, form a semantic meaning, that we call concepts. We group these similar feature patterns by clustering in ``concepts'', that we visualize through Ms-IV. This last method is inspired by Occlusion and Sensitivity analysis (incorporating causality), and uses a novel metric, called Class-aware Order Correlation (CaOC), to globally evaluate the most important image regions according to the model's decision space. We compare our approach to xAI methods such as LIME and Integrated Gradients. Experimental results evince the Ms-IV higher localization and faithfulness values. Finally, qualitative evaluation of combined MAGE and Ms-IV demonstrates humans' ability to agree, based on the visualization, with the decision of clusters' concepts; and, to detect, among a given set of networks, the existence of bias.