ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Deep learning models, particularly deep neural networks, are powerful but often function as "black-boxes" due to their complexity.
• Understanding the relationship between user data and learned representations within these models is crucial but under-researched.

Purpose of the Study:

• To present ShapeShop, an interactive system designed for visualizing and understanding the semantics learned by neural network models.
• To enable users to explore and compare deep learning models and assess the robustness of image classifiers.

Main Methods:

• Developed using standard web technologies for accessibility and broad usability.
• Incorporates interactive visualization techniques to allow users to explore model semantics.
• Facilitates experimentation and comparison of different deep learning models.

Main Results:

• ShapeShop provides a means to demystify deep learning models by visualizing learned representations.
• The system allows users to directly interact with models and their data to gain insights.
• Enables exploration of how models generalize and their robustness to variations in data.

Conclusions:

• Interactive visualization systems like ShapeShop are essential for increasing transparency and understanding in deep learning.
• Facilitating user-driven exploration of model semantics can lead to more trustworthy and robust AI systems.
• Further development can enhance the ability to diagnose and improve deep learning model performance.