This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks (2105.02968v4)

Abstract: Deep neural networks that yield human interpretable decisions by architectural design have lately become an increasingly popular alternative to post hoc interpretation of traditional black-box models. Among these networks, the arguably most widespread approach is so-called prototype learning, where similarities to learned latent prototypes serve as the basis of classifying an unseen data point. In this work, we point to an important shortcoming of such approaches. Namely, there is a semantic gap between similarity in latent space and similarity in input space, which can corrupt interpretability. We design two experiments that exemplify this issue on the so-called ProtoPNet. Specifically, we find that this network's interpretability mechanism can be led astray by intentionally crafted or even JPEG compression artefacts, which can produce incomprehensible decisions. We argue that practitioners ought to have this shortcoming in mind when deploying prototype-based models in practice.

• The paper demonstrates that latent space similarities in prototype-based models fail to align with human-perceived features.
• It shows that adversarial perturbations and JPEG compression artifacts can cause models to misidentify irrelevant image regions.
• The findings call for improved architectures and training strategies to achieve reliable interpretability in critical AI applications.

Shortcomings of Latent Space Prototype Interpretability in Deep Networks

The research paper titled "This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks," authored by Adrian Hoffmann, Claudio Fanconi, Rahul Rade, and Jonas Kohler, presents a thorough examination of the challenges associated with prototype learning-based interpretability in deep neural networks. Specifically, this paper critiques the use of similarities in latent space determinants in models such as the Prototypical Part Network (ProtoPNet).

Overview

Prototype learning methodologies have been proposed as a bridge between traditional black-box models and architectures inherently interpretable by design. These models, particularly ProtoPNet, facilitate decision making by computing similarity scores between latent representations of input data and class-specific prototypes. Despite the broader acceptance and the visual appeal of such a model to a non-expert, the authors argue there exists a profound semantic gap between similarity in latent space (a representation abstracted through layers of convolutional neural networks) and human-perceptible features in input space.

Key Findings

The authors demonstrate that the interpretability promises of models like ProtoPNet are sometimes flawed. The paper presents two primary experiments:

1. Adversarial Perturbations: By introducing minimally human-visible perturbations to input images, they show that prototype networks can easily be confounded to misinterpret regions of interest in an image. Specifically, the tests reveal instances where a model mistakenly identifies irrelevant areas as significant, driven by misinterpretations in latent space similarities.
2. JPEG Compression Artifacts: Utilizing typical JPEG compression—which inherently introduces artifacts—the authors illustrate the fragility of ProtoPNet's interpretability when assessing similarity scores. The network's perception of similarity drastically shifts with the introduction of compression, a factor imperceptible to human observers in most cases.

Implications and Future Directions

The paper renders a significant implication on the field of interpretable machine learning models by challenging the assumption that latent space similarities align with human visual interpretations. In domains where interpretability is paramount, such as in medical imaging, it cautions practitioners against resourcefully deploying interpretability claims linked to similarity-based prototypes. This caution extends to any setting where potential faults in interpretive accuracy, due to adversarial noise or computational corruption via formats like JPEG, could have detrimental impacts.

Future research could explore architectural improvements and training regimes that mitigate the identified fragilities. Robust interpretable models will benefit from further examination of cross-compatibility and resistance against various types of input noise. Furthermore, developing frameworks for enhancing the consistency between latent representation similarities and human-perceptible features can fortify the applicability of prototype-based learning models across diverse, practical environments.

Conclusion

This paper serves as a crucial call to action for improving the reliability of interpretability mechanisms in contemporary AI models, particularly those centralizing on prototype-based reasoning. While they succinctly expose the gaps between latent and input space interpretability, there is an implicit optimism in refining these models. Addressing these weaknesses will ultimately ensure better alignment with human cognitive processes and result in more dependable deployment of AI technologies in critical real-world applications.