Question: What does a good explanation mean for a prototype-based approach?
Evidence: Our method increases the reliability of the explanation through distance and the prototypes. As noted in, the interpretation of previous prototype-based classification methods is difficult because only the Euclidean distance between prototypes and latent representations is given, while having a similar latent representation does not guarantee that images share similarities in human-interpretable features. Therefore, by sampling with distributions and regularizing latent space through VAE, our model constructs dense explanation space that ensures similar latent representations share similarities in appearance. Figure makes this more evident. Looking at the visualized latent space by t-SNE (Van der, embedded data by the previous method tends to separate clusters, often make the case that the distance between two points in the same class farther than the distance between two different class points, as shown in Figure(a). In contrast, the proposed model increases the reliability of explanation through distance by showing that each class is clustered together and exist close to each other that forms dense latent space. Furthermore, by showing latent representations contained within the prototype distribution d i as images, the prototype's reliability can also be increased by their similarities. As illustrated in Figure(d), features within the distribution are similar to the prototype µ i at basic explanation, demonstrating that prototypes are not coincidentally chosen.
We made quantitative comparisons through user study as well as qualitative comparisons to ensure the reliability of the explanation space. We measured the user's understanding through an interactive reconstruction experiment to show the reliability of the explanation space of our method. This experiment is to generate an image similar to a randomly sampled image by changing each dimension value of the latent space through a slide using the decoder of the models. As the user explores the latent space and generates an image similar to the sampled image, we can see how the configuration of the latent space is easy to understand for the user. The evaluation was conducted through Amazon Mechanical Turk. In order to improve the quality of the evaluation, all experiments were conducted through 20 Mechanical Turk Masters. The success rate by model is shown in Table.
Detailed settings for experiments are in Appendix A.2. As concluded that a higher success rate on interactive reconstruction tasks shows improved interpretability, we concluded that the explanation space by our method improved interpretability because the success rate obtained through the interactive reconstruction experiment showed a higher value than the baseline model. The distance value in the explanation space having more intrinsic meaning because the explanation space created by our method showed improved interpretability compared to the baseline model.
Therefore, the experimental results could be used as quantitative evaluation to support that our method created a reliable explanation space.
Our method increases the reliability of the explanation through distance and the prototypes. As noted in, the interpretation of previous prototype-based classification methods is difficult because only the Euclidean distance between prototypes and latent representations is given, while having a similar latent representation does not guarantee that images share similarities in human-interpretable features. Therefore, by sampling with distributions and regularizing latent space through VAE, our model constructs dense explanation space that ensures similar latent representations share similarities in appearance. Figure makes this more evident. Looking at the visualized latent space by t-SNE (Van der, embedded data by the previous method tends to separate clusters, often make the case that the distance between two points in the same class farther than the distance between two different class points, as shown in Figure(a). In contrast, the proposed model increases the reliability of explanation through distance by showing that each class is clustered together and exist close to each other that forms dense latent space. Furthermore, by showing latent representations contained within the prototype distribution d i as images, the prototype's reliability can also be increased by their similarities. As illustrated in Figure(d), features within the distribution are similar to the prototype µ i at basic explanation, demonstrating that prototypes are not coincidentally chosen.
We made quantitative comparisons through user study as well as qualitative comparisons to ensure the reliability of the explanation space. We measured the user's understanding through an interactive reconstruction experiment to show the reliability of the explanation space of our method. This experiment is to generate an image similar to a randomly sampled image by changing each dimension value of the latent space through a slide using the decoder of the models. As the user explores the latent space and generates an image similar to the sampled image, we can see how the configuration of the latent space is easy to understand for the user. The evaluation was conducted through Amazon Mechanical Turk. In order to improve the quality of the evaluation, all experiments were conducted through 20 Mechanical Turk Masters. The success rate by model is shown in Table.
Detailed settings for experiments are in Appendix A.2. As concluded that a higher success rate on interactive reconstruction tasks shows improved interpretability, we concluded that the explanation space by our method improved interpretability because the success rate obtained through the interactive reconstruction experiment showed a higher value than the baseline model. The distance value in the explanation space having more intrinsic meaning because the explanation space created by our method showed improved interpretability compared to the baseline model.
Therefore, the experimental results could be used as quantitative evaluation to support that our method created a reliable explanation space.
Figure 3: Visualizations for the latent space of FashionMNIST by three models. (a) Li et al. (2018)
(b) Proposed model with VAE. (c) Proposed model with FactorVAE.
****Table 1: Success rate by base_x005f_x0002_line model (Li et al., 2018),
proposed model with VAE,
FactorVAE
Success rate
Li et al. 0.27±0.28
VAE 0.48±0.31
FactorVAE 0.54±0.29
Answer: In the prototype-based explanation method, an explanation is given in terms of distance and prototype. A good explanation consists of human-interpretable or human-understandable distance and a prototype that can represent a data set. Since we deal with distances in a latent space, the interpretability of a latent space is the most important factor influencing the explanation. This is because the interpretability of a latent space leads to the interpretability of the distance. Therefore, we show the interpretability of the latent space with Figure.3 and Table.1.