This paper presents Discriminative Part Network (DP-Net), a deep architecture
with strong interpretation capabilities, which exploits a pretrained
Convolutional Neural Network (CNN) combined with a part-based recognition
module. This system learns and detects parts in the images that are
discriminative among categories, without the need for fine-tuning the CNN,
making it more scalable than other part-based models. While part-based
approaches naturally offer interpretable representations, we propose
explanations at image and category levels and introduce specific constraints on
the part learning process to make them more discrimative.

该论文介绍了一种具有较强解释能力的深度架构，名为 Discriminative Part Network (DP-Net)，它利用了预训练的卷积神经网络 (CNN) 和基于部分的识别模块。该系统学习并检测图像中在不同类别中具有区别性的部分，无需对 CNN 进行微调，因此比其他基于部分的模型更具可扩展性。虽然基于部分的方法天然提供可解释性的表达，我们提出了图像和类别层面上的解释，并在部分学习过程中引入特定的约束，使其更具区分度。