Abstract
Explainability of neural network prediction is essential to understand feature importance and gain interpretable insight into neural network performance. In this work, model explanations are fed back to the feed-forward training to help the model generalize better. To this extent, a custom weighted loss where the weights are generated by considering the Euclidean distances between true LIME (Local Interpretable Model-Agnostic Explanations) explanations and model-predicted LIME explanations is proposed. Also, in practical training scenarios, developing a solution that can help the model learn sequentially without losing information on previous data distribution is imperative due to the unavailability of all the training data at once. Thus, the framework known as XAI-Increment incorporates the custom weighted loss developed with elastic weight consolidation (EWC), to maintain performance in sequential testing sets. Finally, the training procedure involving the custom weighted loss shows around 1% accuracy improvement compared to the traditional loss based training for the keyword spotting task on the Google Speech Commands dataset and also shows low loss of information when coupled with EWC in the incremental learning setup.
Abstract (translated)
URL
https://arxiv.org/abs/2211.01413
