Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations (1904.02323v3)

Abstract: Deep learning is increasingly used in decision-making tasks. However, understanding how neural networks produce final predictions remains a fundamental challenge. Existing work on interpreting neural network predictions for images often focuses on explaining predictions for single images or neurons. As predictions are often computed from millions of weights that are optimized over millions of images, such explanations can easily miss a bigger picture. We present Summit, an interactive system that scalably and systematically summarizes and visualizes what features a deep learning model has learned and how those features interact to make predictions. Summit introduces two new scalable summarization techniques: (1) activation aggregation discovers important neurons, and (2) neuron-influence aggregation identifies relationships among such neurons. Summit combines these techniques to create the novel attribution graph that reveals and summarizes crucial neuron associations and substructures that contribute to a model's outcomes. Summit scales to large data, such as the ImageNet dataset with 1.2M images, and leverages neural network feature visualization and dataset examples to help users distill large, complex neural network models into compact, interactive visualizations. We present neural network exploration scenarios where Summit helps us discover multiple surprising insights into a prevalent, large-scale image classifier's learned representations and informs future neural network architecture design. The Summit visualization runs in modern web browsers and is open-sourced.

• The paper introduces Summit, an interactive system that visualizes CNN activations and attributions to clarify complex model behavior on datasets like ImageNet.
• It employs scalable techniques such as activation and neuron-influence aggregation to summarize key neural relationships effectively.
• The open-source tool generates attribution graphs that aid in diagnosing model decisions and guiding the design of interpretable AI systems.

Analyzing Deep Learning Representations with Summit

The paper "Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations" addresses a fundamental challenge in deep learning: understanding the internal workings of neural networks, specifically how they interpret features from complex datasets. This research introduces an interactive visualization tool, Summit, aimed at providing scalable summarization and interpretation of inductive models, tackling interpretability challenges in large-scale dataset models such as InceptionV1 trained on ImageNet.

Core Contributions

The authors outline several significant contributions:

1. Interactive Visualization System (Summit): Summit is designed to interpret entire classes of learned features in convolutional neural networks (CNNs), extended to prevalent models like InceptionV1. It provides an interface for researchers to explore image classifiers' representations.
2. Scalable Summarization Techniques: The paper proposes two main techniques:
   • Activation Aggregation: This method identifies important neurons by summarizing channel activations across a dataset, providing insights into their relative importance.
   • Neuron-Influence Aggregation: This method discovers influential relationships among neurons by computing the influence of one neural layer's channels on another, aiding in understanding precursor-consequence relationships within the model.
3. Attribution Graphs: These are novel visual summaries combining activation and influence aggregation methods, offering graph-based interpretations of how CNNs relate and combine learned features to form hierarchical representations that contribute to outputs.
4. Open-Source Implementation and Accessibility: By providing open-source code and a web-based tool, Summit lowers the barrier to entry for interpretability research, enabling wider access without specialized computational resources.

Methodological Framework

The cornerstone of Summit’s methodology is generating attribution graphs by integrating activation and influence aggregation results. These graphs represent neural networks by treating neurons as vertices and neuron influences as edges, illuminated through Personalized PageRank to emphasize the most impactful neurons in the context of specific class representations. These attribution graphs theoretically and visually capture the neural network's decision-making processes, addressing the opaqueness problem pervasive in complex models.

Insights and Analysis

Summit enables researchers to discover how models discern between similar classes. For instance, comparisons of attribution graphs between classes such as 'polar bear' and 'brown bear' elucidate the model's discriminating features, confirming that color plays a pivotal role, which aligns with human expectations. This comparison showcases the tool's capability in educational and diagnostic applications for interpreting CNNs.

The researchers also pointedly highlight unexpected findings achieved with Summit, such as models detecting non-semantic channels unrelated to task-specific features, further emphasizing the importance of such visualization tools for model diagnostic and refinement tasks.

Implications and Future Directions

By delivering a mechanism to visualize complex model decisions across entire datasets, Summit has practical implications for developing robust and interpretable AI systems. Its applicability in critical domains—where understanding model decisions can prevent adverse outcomes—cannot be understated. The paper's framework can guide future AI systems design, where human-interpretable models are mandated.

Looking forward, potential developments could include expanding Summit’s applicability across diverse neural architectures beyond CNNs and integrating real-time comparative analyses to augment its exploratory power in investigating model biases or failures. Furthermore, as the landscape of AI research shifts towards more algorithmically complex models, Summit offers a blueprint for embedding interpretability within these models, laying the foundation for trust and transparency in AI systems.