In this work, we perform an in-depth analysis of the visualisation methods
implemented in two popular self-explaining models for visual classification
based on prototypes - ProtoPNet and ProtoTree. Using two fine-grained datasets
(CUB-200-2011 and Stanford Cars), we first show that such methods do not
correctly identify the regions of interest inside of the images, and therefore
do not reflect the model behaviour. Secondly, using a deletion metric, we
demonstrate quantitatively that saliency methods such as Smoothgrads or PRP
provide more faithful image patches. We also propose a new relevance metric
based on the segmentation of the object provided in some datasets (e.g.
CUB-200-2011) and show that the imprecise patch visualisations generated by
ProtoPNet and ProtoTree can create a false sense of bias that can be mitigated
by the use of more faithful methods. Finally, we discuss the implications of
our findings for other prototype-based models sharing the same visualisation
method.

本文深入分析了两种流行的自说明模型 ——ProtoPNet 和 ProtoTree 的可视化方法，并使用细粒度数据集进行了验证。通过删除指标的定量证明，证明了 Smoothgrads 或 PRP 等显著性方法提供了更准确的图像补丁，并提出了一种基于物体分割的相关性度量。本研究对其他使用相同可视化方法的原型模型的影响进行了讨论。

原型图像分类中补丁可视化的合理性检查和改进

Sanity checks and improvements for patch visualisation in prototype-based image classification

Existing self-explaining models typically favor extracting the shortest
possible rationales - snippets of an input text "responsible for" corresponding
output - to explain the model prediction, with the assumption that shorter
rationales are more intuitive to humans. However, this assumption has yet to be
validated. Is the shortest rationale indeed the most human-understandable? To
answer this question, we design a self-explaining model, LimitedInk, which
allows users to extract rationales at any target length. Compared to existing
baselines, LimitedInk achieves compatible end-task performance and
human-annotated rationale agreement, making it a suitable representation of the
recent class of self-explaining models. We use LimitedInk to conduct a user
study on the impact of rationale length, where we ask human judges to predict
the sentiment label of documents based only on LimitedInk-generated rationales
with different lengths. We show rationales that are too short do not help
humans predict labels better than randomly masked text, suggesting the need for
more careful design of the best human rationales.

本研究设计了一种自我解释模型 LimitedInk，可以允许用户在任何目标长度上提取原因。用户研究表明，过短的原因不比随机遮盖的文本更有助于人类正确预测标签。

最短的理由是人类理解的最好解释吗？

Are Shortest Rationales the Best Explanations for Human Understanding?

Selecting input features of top relevance has become a popular method for
building self-explaining models. In this work, we extend this selective
rationalization approach to text matching, where the goal is to jointly select
and align text pieces, such as tokens or sentences, as a justification for the
downstream prediction. Our approach employs optimal transport (OT) to find a
minimal cost alignment between the inputs. However, directly applying OT often
produces dense and therefore uninterpretable alignments. To overcome this
limitation, we introduce novel constrained variants of the OT problem that
result in highly sparse alignments with controllable sparsity. Our model is
end-to-end differentiable using the Sinkhorn algorithm for OT and can be
trained without any alignment annotations. We evaluate our model on the
StackExchange, MultiNews, e-SNLI, and MultiRC datasets. Our model achieves very
sparse rationale selections with high fidelity while preserving prediction
accuracy compared to strong attention baseline models.

使用最优传输方法对文本匹配进行有选择的说明性解释，结果产生高度稀疏的对齐，并具有与强注意力基线模型相当的预测准确度。

理性文本匹配：通过最优传输学习稀疏对齐

Rationalizing Text Matching: Learning Sparse Alignments via Optimal  Transport

Most recent work on interpretability of complex machine learning models has
focused on estimating $\textit{a posteriori}$ explanations for previously
trained models around specific predictions. $\textit{Self-explaining}$ models
where interpretability plays a key role already during learning have received
much less attention. We propose three desiderata for explanations in general --
explicitness, faithfulness, and stability -- and show that existing methods do
not satisfy them. In response, we design self-explaining models in stages,
progressively generalizing linear classifiers to complex yet architecturally
explicit models. Faithfulness and stability are enforced via regularization
specifically tailored to such models. Experimental results across various
benchmark datasets show that our framework offers a promising direction for
reconciling model complexity and interpretability.

提出了自说明模型的三个特点 —— 显式性，忠诚度和稳定性，旨在落实模型可解释性并实现复杂模型的解释性，通过特定模型的正则化实现忠诚度和稳定性的要求，实验结果表明，该框架为解决模型的复杂性和可解释性困境提供了一个有前途的方向。