ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models (1704.01942v2)

Abstract: While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models.

• The paper introduces an interactive visualization system that unifies instance- and subset-level analysis to interpret complex deep neural networks.
• It employs a computation graph and neuron activation views to offer clear insights into the architectures and functions of large-scale models.
• The system efficiently handles high-volume data, empowering engineers to identify performance bottlenecks and optimize model parameters.

Visual Exploration of Industry-Scale Deep Neural Network Models

The paper entitled "Visual Exploration of Industry-Scale Deep Neural Network Models" proposes an innovative interactive visualization system designed to provide insights into the complex architectures of deep learning algorithms utilized in industry settings. By addressing various challenges associated with interpreting large-scale deep learning models, the authors have developed a comprehensive tool, referred to as the {}, crafted through participatory design sessions with Facebook engineers and researchers. The implications of their tool extend across both practical and theoretical domains of artificial intelligence, presenting a novel approach to understanding large-scale deep neural networks.

Overview of the Approach

The core contribution of the paper is the interactive visualization system {}, which is designed for the exploration and interpretation of deep neural network models deployed at an industrial scale. This system is notably capable of handling complex model architectures and large datasets, facilitating a more intuitive understanding of deep learning models through a multi-faceted approach. Specifically, the system provides a computation graph overview, a neuron activation view, and supports both instance- and subset-level analyses, thus making the nuanced structure of deep neural networks more accessible for interpretation.

Key Features and Innovations

1. Unified Instance- and Subset-Level Exploration: One of the standout features of is its ability to unify instance- and subset-based inspections. The paper highlights that typical analysis strategies involve both instance-level and subset-level exploration, and the system allows users to visualize and compare activation patterns across multiple instances and instance subsets.
2. Graph Representation of Model Architecture: The system provides a high-level overview of a model's architecture using a computation graph, allowing users to navigate and inspect various components of a complex neural network model. This contributes to a more structured approach to exploring large models, where users can target exploration to parts of the model that are of particular interest.
3. Scalability for Large Datasets: Recognizing that the deployment of machine learning models in industry often involves large-scale datasets, the system is structured to handle high data volumes efficiently. By allowing flexible and dynamic subset definitions, accommodates the exploration of datasets at a higher abstraction level, which aids in computational efficiency and user insight.

Implications and Potential Developments

This visualization system holds significant implications for both practitioners and researchers in artificial intelligence. Practically, it equips engineers and data scientists with a powerful tool for interpreting and debugging models, potentially leading to improved model performance and optimization of parameters. Theoretically, the system fosters deeper understanding of how various data flows through complex neural structures, which could inform the development of new neural network architectures and learning algorithms.

The paper does not claim to be revolutionary but suggests several avenues for future research. These include the extension of visualization techniques to gradients, real-time subset definition, automatic discovery of interesting subsets, and supporting models with input-dependent structures. Furthermore, longitudinal studies to assess how informs the model training process would offer valuable insights into its effectiveness in real-world applications.

In conclusion, the proposed system, , offers a robust solution to the intricacies of visualizing and interpreting deep learning models at an industry scale. By integrating multiple coordinated views and addressing the unique challenges presented by large and complex datasets, the system provides an innovative platform for enhancing the interpretability and applicability of deep learning technologies.