通过限制特征图大小，让CNN更关注物体而非背景，提升可解释性与鲁棒性。
Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation
- 约束特征图空间尺寸，引导网络聚焦关键图像区域
- 在CIFAR-10上达50%以上鲁棒准确率（PGD攻击下）
- 显著改善目标定位精度，比GradCAM等方法提升25%IOU
本文提出Top-GAP，一种新型正则化技术，通过约束卷积神经网络学习到的特征表示的空间尺寸，增强模型的可解释性与鲁棒性。该方法迫使网络聚焦于图像中最显著的区域，有效降低背景干扰。通过对抗攻击和有效感受野分析，证明Top-GAP能将注意力更多集中于物体像素而非背景。在CIFAR-10数据集上，使用PGD攻击（ε=8/255，20轮迭代）时，鲁棒准确率超过50%，同时保持原始干净数据上的准确率。此外，在分布偏移下准确率提升最高达5%。目标定位精度也明显提高，相比GradCAM和Recipro-CAM，交并比（IoU）最高提升25%。
原文摘要 · Abstract (English)
This paper introduces Top-GAP, a novel regularization technique that enhances the explainability and robustness of convolutional neural networks. By constraining the spatial size of the learned feature representation, our method forces the network to focus on the most salient image regions, effectively reducing background influence. Using adversarial attacks and the Effective Receptive Field, we show that Top-GAP directs more attention towards object pixels rather than the background. This leads to enhanced interpretability and robustness. We achieve over 50% robust accuracy on CIFAR-10 with PGD $ε=\frac{8}{255}$ and $20$ iterations while maintaining the original clean accuracy. Furthermore, we see increases of up to 5% accuracy against distribution shifts. Our approach also yields more precise object localization, as evidenced by up to 25% improvement in Intersection over Union (IOU) compared to methods like GradCAM and Recipro-CAM.
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