Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations

TL;DR

This paper introduces explainability tools based on integrated gradients to interpret and optimize deep neural networks, making them more efficient and understandable for use in future in-situ planetary exploration missions.

Contribution

The paper presents a novel explainability tool that ranks and visualizes neuron contributions in DNNs, enabling pruning and efficiency improvements for planetary exploration applications.

Findings

Neurons can be ranked by their contribution to classification accuracy.

Pruning high-rank neurons improves network efficiency.

Explainability tools facilitate validation and verification of DNNs.

Abstract

Deep learning (DL) has proven to be an effective machine learning and computer vision technique. DL-based image segmentation, object recognition and classification will aid many in-situ Mars rover tasks such as path planning and artifact recognition/extraction. However, most of the Deep Neural Network (DNN) architectures are so complex that they are considered a 'black box'. In this paper, we used integrated gradients to describe the attributions of each neuron to the output classes. It provides a set of explainability tools (ET) that opens the black box of a DNN so that the individual contribution of neurons to category classification can be ranked and visualized. The neurons in each dense layer are mapped and ranked by measuring expected contribution of a neuron to a class vote given a true image label. The importance of neurons is prioritized according to their correct or incorrect contribution to the output classes and suppression or bolstering of incorrect classes, weighted by the size of each class. ET provides an interface to prune the network to enhance high-rank neurons and remove low-performing neurons. ET technology will make DNNs smaller and more efficient for implementation in small embedded systems. It also leads to more explainable and testable DNNs that can make systems easier for Validation \& Verification. The goal of ET technology is to enable the adoption of DL in future in-situ planetary exploration missions.