Feature visualization is used to visualize learned features for black box machine learning models. Our approach explores an altered training process to improve interpretability of the visualizations. We argue that by using background removal techniques as a form of robust training, a network is forced to learn more human recognizable features, namely, by focusing on the main object of interest without any distractions from the background. Four different training methods were used to verify this hypothesis. The first used unmodified pictures. The second used a black background. The third utilized Gaussian noise as the background. The fourth approach employed a mix of background removed images and unmodified images. The feature visualization results show that the background removed images reveal a significant improvement over the baseline model. These new results displayed easily recognizable features from their respective classes, unlike the model trained on unmodified data.

本研究利用背景删除技术作为强健训练的方式, 探索性地修改了训练过程，从而提高了黑盒子机器学习模型特征可视化的解释能力，并验证了此假设通过四种不同的训练方法。图像的特征可视化结果表明，使用背景删除图像比使用未修改数据的模型的改进更为显著。

有针对性的背景去除创造了可解释的特征可视化