Scaling Vision Models Does Not Improve Their Mechanistic Interpretability

Core Concepts

Scaling vision models in terms of dataset and model size does not improve their mechanistic interpretability at the individual unit level.

Abstract

The authors conducted a large-scale psychophysical study to investigate the mechanistic interpretability of individual units across nine different computer vision models. The models varied in terms of their scale (dataset and model size), architecture, and training objectives.

The key findings are:

1. Scaling models and datasets does not lead to improved mechanistic interpretability. Neither larger models nor models trained on larger datasets were found to be more interpretable than older, smaller models like GoogLeNet.

2. Feature visualizations, a common method for explaining unit activations, are less helpful for understanding unit behavior compared to using natural exemplars, regardless of the model.

3. The position of a unit within the network's layers is sometimes predictive of its interpretability, with later layers being more interpretable in models trained on large-scale datasets.

4. When the task difficulty is increased even slightly, human performance drops dramatically, suggesting that the mechanistic interpretability of these models is quite limited.

The authors release the dataset collected through their psychophysical experiments, called ImageNet Mechanistic Interpretability (IMI), to enable further research on developing automated interpretability measures that can guide the design of more interpretable vision models.

Stats

"Scaling models and datasets does not lead to improved mechanistic interpretability."
"Feature visualizations are less helpful for understanding unit behavior compared to using natural exemplars, regardless of the model."
"The position of a unit within the network's layers is sometimes predictive of its interpretability, with later layers being more interpretable in models trained on large-scale datasets."
"When the task difficulty is increased even slightly, human performance drops dramatically, suggesting that the mechanistic interpretability of these models is quite limited."

Quotes

"Scaling has not led to increased interpretability. Therefore, we argue that one has to explicitly optimize models to be interpretable."
"Latest-generation vision models appear even less interpretable than older architectures, hinting at a regression rather than improvement, with modern models sacrificing interpretability for accuracy."
"We expect our dataset to enable building automated measures for quantifying the interpretability of models and, thus, bootstrap the development of more interpretable models."

Key Insights Distilled From

Scale Alone Does not Improve Mechanistic Interpretability in Vision Models

by Roland S. Zi... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2307.05471.pdf

Deeper Inquiries

How can we design vision models that are inherently more interpretable at the mechanistic level, beyond just scaling up existing architectures?

To design vision models that are inherently more interpretable at the mechanistic level, we need to focus on intentional design choices that prioritize interpretability alongside performance. One approach is to incorporate interpretability as a core objective during the model architecture design phase. This involves structuring the model in a way that individual units and layers have clear and understandable functions. For example, designing models with modular components that correspond to specific visual features or concepts can enhance interpretability.
Additionally, utilizing techniques such as sparse coding, where each unit is responsible for detecting a specific feature, can improve interpretability by making it easier to understand the role of each unit in processing visual information. Incorporating constraints during training that encourage the emergence of interpretable features can also be beneficial. For instance, enforcing sparsity or encouraging units to learn disentangled representations can lead to more interpretable models.
Furthermore, leveraging insights from neuroscience on how biological visual systems process information can inspire the design of more interpretable artificial vision models. By mimicking the hierarchical structure and functional principles of the human visual system, we can create models that align more closely with human perception and are therefore more interpretable.
In essence, designing inherently interpretable vision models requires a holistic approach that integrates interpretability considerations into the model design process from the outset, rather than relying solely on scaling up existing architectures.

How might insights from neuroscience on the information processing in biological visual systems inform the design of more interpretable artificial vision models?

Insights from neuroscience on the information processing in biological visual systems can provide valuable guidance for designing more interpretable artificial vision models. By studying how the human brain processes visual information, researchers can gain a deeper understanding of the underlying principles that govern interpretability in vision systems.
One key aspect that can inform the design of interpretable artificial vision models is the concept of hierarchical processing. The human visual system is organized in a hierarchical manner, with lower-level neurons detecting simple features like edges and textures, while higher-level neurons integrate these features to recognize complex objects and scenes. By emulating this hierarchical structure in artificial vision models, we can create systems that operate in a more interpretable manner, where each layer or unit corresponds to a specific level of abstraction in visual processing.
Moreover, the idea of receptive fields, as observed in biological neurons, can also be applied to artificial vision models to enhance interpretability. By designing units that respond selectively to specific visual patterns or features, similar to how neurons in the visual cortex have specific receptive fields, we can create models that are more transparent in their decision-making processes.
Additionally, understanding how the brain processes visual information in a sparse and distributed manner can inspire the design of artificial vision models that prioritize sparse and disentangled representations. By encouraging models to learn sparse and disentangled features, we can improve interpretability by making it easier to attribute model decisions to specific visual cues.
In summary, insights from neuroscience can serve as a blueprint for designing artificial vision models that are not only high-performing but also inherently interpretable by aligning with the principles of hierarchical processing, receptive fields, and sparse coding observed in biological visual systems.

What are the potential trade-offs between model performance and mechanistic interpretability, and how can we find the right balance?

There are inherent trade-offs between model performance and mechanistic interpretability in artificial vision models. As models become more complex and achieve higher performance on tasks like image classification, they often sacrifice interpretability, making it challenging to understand how decisions are made at a mechanistic level.
One trade-off is the complexity of the model architecture. Highly complex models with numerous layers and parameters may achieve superior performance but can be difficult to interpret due to the intricate interactions between components. Simplifying the model architecture to enhance interpretability may lead to a decrease in performance as the model loses the capacity to capture nuanced patterns in the data.
Another trade-off arises from the training process. Models trained on large and diverse datasets tend to perform better on a wide range of tasks but may lack interpretability because the learned representations are highly abstract and difficult to decipher. On the other hand, models trained on smaller, more curated datasets for interpretability may struggle to generalize to unseen data and tasks.
Finding the right balance between model performance and mechanistic interpretability requires a nuanced approach. One strategy is to incorporate interpretability constraints during model training, such as regularization techniques that encourage the emergence of interpretable features. By optimizing models for both performance and interpretability simultaneously, we can mitigate the trade-offs and create models that are both accurate and transparent in their decision-making processes.
Furthermore, leveraging post-hoc interpretability methods, such as feature visualization and attribution techniques, can provide insights into model behavior without compromising performance. These methods allow researchers to probe model predictions and understand how specific inputs influence the output, offering a compromise between performance and interpretability.
Ultimately, striking the right balance between model performance and mechanistic interpretability requires a careful consideration of the specific use case and goals of the model. By prioritizing interpretability as a design objective and exploring techniques that enhance transparency without significantly impacting performance, researchers can navigate the trade-offs and develop models that are both accurate and interpretable.