让卷积模型更懂自己为何这么判断，无需标注像素。
Batch-CAM: Introduction to better reasoning in convolutional deep learning models
- 用批量注意力图引导训练，自动聚焦关键特征。
- 生成的热力图更清晰可读，误激活显著减少。
- 适合需要解释性的高风险场景，如医疗诊断。
深度学习模型的不透明性常阻碍其在高风险领域的应用。我们提出一种训练框架，使模型关注与类别代表性特征对齐，无需像素级标注。为此，引入了批量梯度加权类激活映射（Batch-CAM），一种向量化实现，可直接嵌入训练循环，计算开销极小。设计两种正则化项：原型损失（Prototype Loss）使单样本注意力与全局类别均值对齐；批量注意力损失（Batch-CAM Loss）确保批次内一致性。采用L1、L2和SSIM指标评估。在MNIST和Fashion-MNIST上使用ResNet18与ConvNeXt-V2验证，相比基线方法，本方法生成的显著性图更连贯且人类可读。在保持竞争性分类准确率的同时，定性重建分析表明模型有效抑制了虚假特征激活。Batch-CAM通过利用批量统计信息指导特征提取，为训练内在可解释模型提供了一条可扩展路径，有效弥合预测性能与可解释性之间的差距。
原文摘要 · Abstract (English)
Deep learning opacity often impedes deployment in high-stakes domains. We propose a training framework that aligns model focus with class-representative features without requiring pixel-level annotations. To this end, we introduce Batch-CAM, a vectorised implementation of Gradient-weighted Class Activation Mapping that integrates directly into the training loop with minimal computational overhead. We propose two regularisation terms: a Prototype Loss, which aligns individual-sample attention with the global class average, and a Batch-CAM Loss, which enforces consistency within a training batch. These are evaluated using L1, L2, and SSIM metrics. Validated on MNIST and Fashion-MNIST using ResNet18 and ConvNeXt-V2, our method generates significantly more coherent and human-interpretable saliency maps compared to baselines. While maintaining competitive classification accuracy, the framework successfully suppresses spurious feature activation, as evidenced by qualitative reconstruction analysis. Batch-CAM appears to offer a scalable pathway for training intrinsically interpretable models by leveraging batch-level statistics to guide feature extraction, effectively bridging the gap between predictive performance and explainability.
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