Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its widespread adoption has been limited due to a reliance on tricks to generate interpretable images, and corresponding challenges in scaling it to deeper neural networks. Here, we describe MACO, a simple approach to address these shortcomings. The main idea is to generate images by optimizing the phase spectrum while keeping the magnitude constant to ensure that generated explanations lie in the space of natural images. Our approach yields significantly better results (both qualitatively and quantitatively) and unlocks efficient and interpretable feature visualizations for large state-of-the-art neural networks. We also show that our approach exhibits an attribution mechanism allowing us to augment feature visualizations with spatial importance. We validate our method on a novel benchmark for comparing feature visualization methods, and release its visualizations for all classes of the ImageNet dataset on https://serre-lab.github.io/Lens/. Overall, our approach unlocks, for the first time, feature visualizations for large, state-of-the-art deep neural networks without resorting to any parametric prior image model.

翻译：特征可视化已获得广泛普及，尤其在Olah等人2017年具有影响力的工作之后，该方法被确立为可解释性的关键工具。然而，由于其依赖技巧生成可解释图像，且在扩展到更深层神经网络时面临相应挑战，其广泛应用受到限制。本文提出了MACO这一简单方法来解决上述不足。核心思路是通过优化相位谱同时保持幅度恒定来生成图像，确保生成的解释图像位于自然图像空间内。我们的方法在定性和定量上均取得显著更优的结果，并为大规模先进神经网络解锁了高效且可解释的特征可视化。我们进一步表明，该方法具备归因机制，可对特征可视化赋予空间重要性。我们在新型特征可视化方法对比基准上验证了本方法，并发布了ImageNet数据集所有类别的可视化结果（https://serre-lab.github.io/Lens/）。总体而言，本方法首次在不借助任何参数化先验图像模型的情况下，实现了大规模先进深度神经网络的特征可视化。