Machine learning is in various flavors being deployed in a wide range of application domains, including autonomous vehicles, robot navigation, interaction systems and even medicine. As with most large-scale data driven approaches it is in most practical cases, e.g. in deep learning architectures, hard to analyze and understand what is the underlying learned model, how accurate is it, and exactly how it is solved?

In this project, we will develop interactive visualization methods making both the learning process and the efficacy of the solution transparent to developers and users. Using deep learning and computer vision tasks as the initial application domain we will take a holistic approach and aim to investigate ways of visualizing aspects of the training data, network structures, and inference results jointly in the same framework.
Visualisering för förståelse och utveckling av maskininlärning - deep learning.
The visualization system will combine new and traditional data visualization methods with novel holistic approaches specific to visualization of machine learning systems and target the needs of both developers and systems architects. For developers, the visualizations will be integrated into an environment with tools for analyzing training data quality and feature variation, as well as the performance of the system.

Enabling informed choices

Intuitive visual models will enable informed choices when data is collected and the architecture is trained. Another key challenge is to analyze and quantify the domain shift when training and validation data are from different sources. For systems architects, the tool-set will act as a visual analysis and debugging tool for architecture design and systems analysis. In our prototype system, we will include an integrated development environment with interfaces to, and visual debugging tools for, machine learning libraries such as TensorFlow. New tools for high level architecture design and analysis is one of the most important challenges in the development of next generation machine learning algorithms.


External partners

School of Computer Science and Engineering, NTU, Singapore

Jianmin Zheng,


Cover of publication ''
Gabriel Eilertsen, Joel Kronander, Gyorgy Denes, Rafal K. Mantiuk, Jonas Unger (2017)

ACM Transactions on Graphics , Vol.36 Continue to DOI

WASP research at MIT


Our research environment is located at the division of Media and Information Technology on Campus Norrköping. Our work draws on 20 years of research, focusing on visualization and interaction in the context of AI, autonomous systems and software.