Question: Are baselines missing in the evaluation section?
Evidence: Explanation Methods for DNN While there are diverse suites of methods to explain DNN, it can be categorized based on the access of the model's internal information and how they provide explanations. The model-agnostic method is to approximate the relationships between input data and output decision while treating the model as a black-box. There are methods to create a local explanation by selecting features that play an essential role in making decisions and methods providing an interpretable model through a global approximate. On the other hand, in knowing the internal information of the model, the model-transparent method provides explanations by saliency map using gradient in classifier, class activation map using global average pooling, and feature map based on attention. The third method provides explanations by examples capturing relationship between test input and the model's decision.
Considering that examples are based on training data or learned prototype data, our work is closely aligned with these prototype-based classification models. The example-based method shows its efficacy to explain complex concepts in psychology and education domains, and is also accepted by the general public as the most preferred explanation method to explain.
The following three prototype-based classification methods are similar to our method in that they use the distance between the encoded input and prototypes for classification and explanation. learn and visualize the prototypes for the entire data through autoencoder.  go further from here, characterizing training image patches as class-specific prototypes for partial explanations, while feature a hierarchical demonstration of prototypes through related groups for non-flat explanations. As most of prototype-based explanation methods are based on, we use it as a baseline model. Our framework goes further from these methods, expands prototypes from vectors to distribution in latent space and constructs explanation space for diverse explanations with reliability. In addition to prototype-based methods, there are several methods that use the distance in latent space to understand the internal behavior of a model or to provide explanation using VAE or GAN.
The largest body of research on estimating uncertainty in deep learning is Bayesian neural networks.
To obtain predictive uncertainty, Bayesian neural networks use prior distributions on model parameters and infer the posterior distribution. Although exact inference in Bayesian methods is intractable, a range of approximate techniques has been proposed, such as variational inference and stochastic gradient hamiltonian monte carlo (SG-HMC). However, in practice, these Bayesian methods are outperformed by Deep Ensembles. This non-bayesian technique uses multiple neural networks trained from different initializations and averages their predictions as the model output. It is simple and effective but requires high computational costs at training and test time. Another non-bayesian technique is based on evidential deep learning, which we use to estimate uncertainty. These methods use a single deterministic neural network and apply prior distributions on model predictions, not model parameters.
As most of prototype-based explanation methods are based on Li et al. (2018), we use it as a baseline model.
Answer: In our paper, the comparison with prior work is written in section 4.1. As mentioned in the Introduction, we wanted to develop an inherently interpretable model. Among these inherently interpretable models, we wanted to create a model that provides explanations through examples that are known as powerful explanations. That's why we created a prototype-based explanation model, as shown in Section 2 Explanation Methods for DNN.

In this process, since prototype-based DNNs are mostly made based on the method of [7], there is a problem that similar latent representations do not share a similar appearance. We developed a model to solve these common problems and expand prototype-based explanations. Therefore, as shown in the second paragraph of Explanation Methods for DNN in Section 2, we compared with [7], the paper used as the basis for prototype-based DNN, as a baseline.

Therefore, comparison with the method used as the baseline was carried out in section 4.1, and comparison with methods corresponding to the post-hoc explanation was not carried out. After comparison with the prior work, it was shown that the additional explanations obtained with our method aided the understanding of the judgment of the model in section 5.