Counterfactual Explanation Based on Gradual Construction for Deep Networks

Abstract

To understand the black-box characteristics of deep networks, counterfactual explanation that deduces not only the important features of an input space but also how those features should be modified to classify input as a target class has gained an increasing interest. The patterns that deep networks have learned from a training dataset can be grasped by observing the feature variation among various classes. However, current approaches perform the feature modification to increase the classification probability for the target class irrespective of the internal characteristics of deep networks. This often leads to unclear explanations that deviate from real-world data distributions. To address this problem, we propose a counterfactual explanation method that exploits the statistics learned from a training dataset. Especially, we gradually construct an explanation by iterating over masking and composition steps. The masking step aims to select an important feature from the input data to be classified as a target class. Meanwhile, the composition step aims to optimize the previously selected feature by ensuring that its output score is close to the logit space of the training data that are classified as the target class. Experimental results show that our method produces human-friendly interpretations on various classification datasets and verify that such interpretations can be achieved with fewer feature modification.

arXiv commentThe first two authors contributed equally to this work

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
• Axiomatic Attribution for Deep Networks
• Learning Important Features Through Propagating Activation Differences
• Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
• This Looks Like That: Deep Learning for Interpretable Image Recognition
• Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
• Explaining Image Classifiers by Counterfactual Generation
• Towards Interpretable and Robust Hand Detection via Pixel-wise Prediction

Cited by in corpus (5)

• A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
• Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
• Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
• ECINN: Efficient Counterfactuals from Invertible Neural Networks
• On Quantitative Evaluations of Counterfactuals