Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers (1801.06889v3)

Abstract: Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where). We conclude by highlighting research directions and open research problems. This survey helps researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.

• The paper introduces an interrogative framework that systematically categorizes visual analytics roles to enhance deep learning interpretability and debugging.
• It details visualization targets and methods, including network architecture, dimensionality reduction techniques, and instance-based analysis.
• The study emphasizes the need for scalable, ethical, and user-centered analytics approaches to advance model development and post-training evaluation.

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

This paper provides a comprehensive survey on the integration of visual analytics within deep learning, structured around the interrogative framework of the Five W's and How: Why, Who, What, How, When, and Where. Authored by Hohman et al., the survey systematically examines the role and potential of visual analytics tools in the field of deep learning, highlighting current achievements and identifying areas for further research.

Key Contributions and Insights

The primary contribution of this survey is its structured, interrogative approach to categorize and understand the growing domain of visual analytics in deep learning. This approach allows a nuanced exploration of the motivations, user groups, visualization targets, methods, and application scenarios associated with visual analytics in deep learning.

Why: Interpretability and Debugging

One of the foremost motivations for incorporating visual analytics into deep learning is model interpretability and explainability. Given the complexity of deep neural networks, achieving transparency is challenging but essential for model validation and user trust. Additionally, visual analytics facilitates model debugging and improvement by allowing developers to iterate and optimize network architectures through real-time insights.

Who: Diverse User Groups

The survey identifies three primary user groups benefiting from visual analytics: model developers, model users, and non-experts. Developers and builders use these tools for detailed inspection and debugging, while non-experts benefit from educational interfaces that demystify deep learning concepts.

What and How: Visualization Targets and Techniques

The paper categorizes visualization targets, such as network architecture, learned parameters, and aggregated metrics. Methods include node-link diagrams for architectural visualization, dimensionality reduction for visualizing embeddings, and instance-based analysis for understanding neuron activations.

When and Where: Process Integration and Community

Visual analytics is useful both during and after training phases. It allows for dynamic monitoring of model training as well as post-training analysis and visualization for interpretability purposes. The research community is vibrant, with contributions from both academic and industry sectors, leading to a flourishing ecosystem of open source tools and frameworks.

Implications and Future Directions

The survey outlines several promising research directions. Enhancing interpretability remains a critical focus, with the need for more sophisticated visual representations and interaction techniques. The integration of human-centered methodologies to improve system usability and ensure transparency in AI systems is also emphasized.

Scalability challenges persist in handling large models and datasets, necessitating improved computation methods and visualization frameworks. Attention should also be given to examining bias in models, promoting ethical AI usage, and augmenting human decision-making through enhanced visual analytics.

Conclusion

This paper contributes significantly to the interdisciplinary field of visual analytics in deep learning, offering a clear, structured approach to understanding and advancing the field. By elucidating the current landscape and identifying research gaps, it serves as a valuable resource to guide future developments in AI interpretability and visualization.