NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis

Abstract

Fig. 1. Our visual steering interface purposed to guide analysts in the task of constructing the best performing deep neural network architecture for a given application using a one-shot search algorithm. The first section is the Lego View where the analyst can create and edit different components of a large neural network with simple drag and drop operations. The Lego View visualizes the different neural network components along with their parameters. An initial large neural network is treated as a super graph (shown in the Graph View) and the problem of finding the best performing neural network architecture is framed as searching for the respective subgraph in this super graph. The Graph View visualizes the super graph where each node is a block (sequence of neural network components). The One-Shot Search algorithm evaluates the subgraphs of this super graph iteratively, gauges their accuracy with regards to a test dataset and provides a fitness score for each node in the graph (Block Information view). The subgraphs are then projected as points into the scatterplot in the Search Space view and colored based on their evaluation accuracy. Analysts can filter and analyze a specific region in the subgraph search space with zoom and pan operations in the Search Space View. Finally, all blocks with high fitness scores are combined to create the best performing candidate neural network architecture.

PC-Expo: A Metrics-Based Interactive Axes Reordering Method for Parallel Coordinate Displays

“…Third, there is also a growing interest in explainability [8,48,49,52] in modern computer-human interaction systems. For visualization frameworks that are deployed for high-liability applications (e.g., crimefighting), explainability is a crucial feature.…”