Question: Why generalizing to larger images and complex datasets is out of the scope for validating the scalability of the proposed network structure when there are no particular blockers from training such networks?
Evidence: The explanations of our model are divided into basic explanation and explanations of the basic explanation. For fundamental explanations, visualized images of the learned prototypes and the distance between encoded input and prototype distributions are given as previous prototype-based explanation methods. To explain this basic explanation, we additionally provide two gradual explanations. These explanations can be obtained because our method constructs dense explanation space, unlike previous methods. As a result, it is possible to generate interconnection between latent representations as images. We represent changing elements by interpolating these latent representations and define them as relationships.
The relationships between inputs and prototypes can be visualized by our model. In situations where distance is given for interpretable information, the factors that can reduce the distance are critical for understanding the model's prediction. Additionally, relationships between prototypes are given. Since the prototypes are representative of the whole dataset, relationships between prototypes show which features are needed to change from one class to another class. This explanation can be used as basic information about how the model discriminates classes.
After the model is trained, basic explanation and explanations for basic explanation are visualized as follows. First, the prototype, provided as the fundamental explanation, is visualized by sending µ i of the prototype distribution through the decoder g as a prototype. Next, the relationships between prototype µ i and prototype µ j are visualized as sending µ i + (µ j − µ i )r/k for r = 0, 1, . . . , k through the decoder g, which can be controlled by increasing the value of k for detailed explanation and decreasing the value of k for a brief explanation. Finally, the relationships between inputs and prototypes are provided in the same way, and visualizations of prototype distributions are based on µ values and σ values.
The disadvantage of our model comes from this visualization. Many training samples are required to generate explanation images. In addition, visualized images of our explanations are fundamentally generated through VAE, making it challenging to generate human-recognizable images for complex datasets that are difficult for VAE to generate the observable image. However, this is a problem related to VAE's expressive power, and there are many high-resolution expressive methods based on VAE, so applying these methods would solve these problems in future work.
The disadvantage of our model comes from this visualization. Many training samples are required to generate explanation images. In addition, visualized images of our explanations are fundamentally generated through VAE, making it challenging to generate human-recognizable images for complex datasets that are difficult for VAE to generate the observable image.
Answer: We also agree that the scalability of the proposed network structure is necessary. However, please note that the scalability issue can be dealt with as in Answer #1. Since our paper showed the advantages of using the latent space of a VAE structure in prototype-based explanation methods, we also believe that ImageNet, a larger image dataset such as the one you suggested, is a natural next step.
Since the scalability issue is important, we will mention the same comment as Answer #1 in the manuscript. We will add this to the last paragraph of section 4.3, which deals with the limitations of our paper.