Leveraging counterfactual concepts for debugging and improving CNN model performance

TL;DR

This paper introduces a novel method that uses counterfactual explanations to identify key filters in CNNs, retrain models to focus on relevant features, and improve image classification accuracy by 1-2%.

Contribution

It presents a new approach leveraging counterfactual reasoning to enhance CNN performance through targeted filter activation and retraining strategies.

Findings

Improved CNN accuracy by 1-2% on benchmark datasets.

Effective identification of important filters for each class.

Insights into model weaknesses and biases.

Abstract

Counterfactual explanation methods have recently received significant attention for explaining CNN-based image classifiers due to their ability to provide easily understandable explanations that align more closely with human reasoning. However, limited attention has been given to utilizing explainability methods to improve model performance. In this paper, we propose to leverage counterfactual concepts aiming to enhance the performance of CNN models in image classification tasks. Our proposed approach utilizes counterfactual reasoning to identify crucial filters used in the decision-making process. Following this, we perform model retraining through the design of a novel methodology and loss functions that encourage the activation of class-relevant important filters and discourage the activation of irrelevant filters for each class. This process effectively minimizes the deviation of activation patterns of local predictions and the global activation patterns of their respective inferred classes. By incorporating counterfactual explanations, we validate unseen model predictions and identify misclassifications. The proposed methodology provides insights into potential weaknesses and biases in the model's learning process, enabling targeted improvements and enhanced performance. Experimental results on publicly available datasets have demonstrated an improvement of 1-2\%, validating the effectiveness of the approach.