Local vs. Distributed Representations in Deep Neural Networks: A Human-Centric Evaluation of Interpretability

Core Concepts

Distributed representations in deep neural networks are more interpretable than local representations, especially in deeper layers, as they are easier for humans to understand and are more heavily relied upon by the model for decision-making.

Abstract

• Bibliographic Information: Colin, J., Goetschalckx, L., Fel, T., Boutin, V., Gopal, J., Serre, T., & Oliver, N. (2024). Local vs distributed representations: What is the right basis for interpretability? arXiv preprint arXiv:2411.03993.
• Research Objective: This paper investigates whether local (single neuron) or distributed representations provide a better basis for the interpretability of deep neural networks.
• Methodology: The authors conducted three large-scale psychophysics experiments with 560 participants to evaluate the interpretability of features derived from both local and distributed representations in a ResNet50 model trained on ImageNet. They adapted an experimental protocol where participants were shown sets of images representing a feature and asked to identify a query image sharing the same visual elements. The visual coherence of the feature, measured by the proportion of correct identifications, served as a proxy for interpretability. Additionally, they analyzed the importance of each feature by measuring the drop in logit score when the feature was ablated.
• Key Findings: The results consistently showed that features derived from distributed representations were significantly easier for humans to interpret than those from local representations, particularly in deeper layers of the network. Furthermore, the model relied more heavily on features from distributed representations for decision-making.
• Main Conclusions: The study provides strong evidence that distributed representations offer a superior basis for the interpretability of deep neural networks compared to local representations. This suggests a need to shift focus from interpreting individual neurons to analyzing sparsely distributed representations for better understanding and explaining model behavior.
• Significance: This research contributes significantly to the field of Explainable AI (XAI) by providing empirical evidence for the superiority of distributed representations in understanding deep learning models.
• Limitations and Future Research: The study acknowledges limitations regarding potential semantic confounding variables in the experimental design and the use of visual coherence as a proxy for interpretability. Future research could address these limitations by refining the experimental protocol and exploring more direct measures of interpretability.

Stats

Participants in the distributed representation condition achieved an average accuracy of 83.5% compared to 78.8% in the local representation condition in Experiment I.
Experiment II, controlling for semantic confounders, showed similar trends with distributed representations outperforming local representations.
Feature importance analysis revealed that the model relied significantly more on features derived from distributed representations than local representations, with a statistically significant difference (z = -5.86, p < .001, Mann-Whitney U Test).

Quotes

Key Insights Distilled From

Local vs distributed representations: What is the right basis for interpretability?

by Julien Colin... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.03993.pdf

Deeper Inquiries

How can the experimental protocol be further refined to more accurately measure the interpretability of features, moving beyond visual coherence as a proxy?

While visual coherence, as assessed in the described experiments, provides a valuable starting point for evaluating feature interpretability, it primarily captures the ambiguity or perceptual similarity of visual patterns. To move beyond this proxy and delve deeper into genuine interpretability, several refinements can be implemented:

Incorporate Procedural Learning and Feedback: True understanding often involves a process of learning and refinement. Instead of single-trial assessments, the protocol could be redesigned to incorporate multiple rounds where participants interact with features, receive feedback on their interpretations, and adjust their understanding accordingly. This could involve tasks like predicting the impact of image manipulations on feature activations or generating novel images that strongly activate a specific feature.

Elicit Explicit Explanations: Instead of relying solely on image selection, participants could be prompted to provide explicit verbal or written explanations for their choices. This would offer richer insights into their thought processes and reveal the reasoning behind their feature interpretations. Analyzing these explanations could uncover whether participants focus on semantically meaningful aspects or lower-level visual attributes.

Explore Feature Compositionality: A hallmark of human understanding is the ability to decompose complex concepts into simpler, constituent parts. The protocol could be extended to assess whether participants can identify and reason about the compositionality of distributed representations. For instance, they could be tasked with predicting the activation of a feature based on the activations of its presumed sub-components.

Evaluate Generalization Beyond ImageNet: The current study focuses on ImageNet, which, while extensive, might not fully represent the diversity of real-world visual tasks. Evaluating feature interpretability across a wider range of datasets, particularly those involving more abstract or specialized domains, would provide a more comprehensive assessment of the generalizability of the findings.

Control for Semantic Bias More Effectively: As acknowledged in the paper, semantic confounds pose a challenge. While Experiment II attempts to mitigate this, further refinements are needed. This could involve using datasets with more controlled semantic relationships between classes or developing novel techniques to isolate and manipulate semantic information within the stimuli.

By incorporating these refinements, future research can move beyond visual coherence as a proxy and develop a more robust and nuanced understanding of feature interpretability in deep neural networks.

Could there be specific tasks or datasets where local representations might still hold an advantage for interpretability, or are distributed representations universally superior?

While the study presents compelling evidence for the superiority of distributed representations for interpretability in the context of image classification with ImageNet, it's premature to conclude their universal superiority over local representations. Several scenarios could exist where local representations might still hold an advantage:

Tasks with Explicit Feature Engineering: In domains where features are carefully hand-designed and engineered to encode specific, interpretable attributes (e.g., medical imaging with handcrafted features for tumor detection), local representations might align more directly with these pre-defined features, leading to more straightforward interpretations.

Datasets with Limited Complexity: For tasks involving simpler datasets with fewer classes and less intra-class variability, the superposition problem might be less pronounced. In such cases, local representations might adequately capture the underlying features without the need for distributed representations.

Focus on Specific Neuron Subsets: Even within networks primarily relying on distributed representations, certain subsets of neurons might exhibit strong selectivity for highly interpretable features. Identifying and focusing on these specific neurons could still provide valuable insights, even if the overall representation is distributed.

Early Layers in Hierarchical Networks: As observed in the study, the benefits of distributed representations become more pronounced in deeper layers. In earlier layers, where features are often more localized and edge-like, local representations might still offer a reasonable degree of interpretability.

Hardware Constraints: Distributed representations, while offering interpretability advantages, often come at the cost of increased computational complexity and memory requirements. In resource-constrained settings, local representations might provide a more practical trade-off between interpretability and computational efficiency.

Therefore, the choice between local and distributed representations for interpretability depends on a complex interplay of factors, including the specific task, dataset characteristics, model architecture, and available resources. A nuanced perspective considering these factors is crucial for determining the most suitable approach.

What are the implications of these findings for the development of more transparent and trustworthy AI systems in real-world applications?

The findings highlighting the superiority of distributed representations for interpretability have significant implications for developing more transparent and trustworthy AI systems in real-world applications:

Shifting Focus in Explainable AI (XAI): The study underscores the need for the XAI field to move beyond interpreting individual neurons and embrace the analysis of distributed representations. Future XAI methods should be designed to effectively extract, visualize, and communicate the meaning encoded within these more complex representations.

Enhancing Human-AI Collaboration: By providing a more human-intelligible basis for understanding model decisions, distributed representations can foster better human-AI collaboration. This is particularly crucial in domains like healthcare, finance, and law, where understanding the rationale behind AI-driven recommendations is essential for building trust and ensuring responsible use.

Facilitating Model Debugging and Improvement: Interpretable distributed representations can act as valuable tools for model debugging and improvement. By understanding which features contribute to accurate and erroneous predictions, developers can identify biases, address limitations, and refine models for enhanced performance and fairness.

Enabling Regulatory Compliance and Auditing: As AI systems become increasingly integrated into critical domains, regulatory frameworks demanding transparency and accountability are emerging. The ability to interpret and audit AI models based on distributed representations can facilitate compliance with these regulations and foster public trust in AI technologies.

Driving New Research Directions: The study opens up exciting new research avenues in XAI. This includes developing novel methods for disentangling and visualizing distributed representations, exploring their compositionality and causal relationships, and investigating their role in different learning paradigms beyond supervised classification.

By embracing the insights from this study and prioritizing the development and deployment of AI systems grounded in interpretable distributed representations, we can pave the way for more transparent, trustworthy, and human-centered AI applications across diverse domains.