Parallel embeddings
Dustin Arendt, Nasheen Nur, Zhuanyi Huang, Gabriel Fair, Wenwen Dou · 2020
We introduce "Parallel Embeddings", a new technique that generalizes the classical Parallel Coordinates visualization technique to sequences of learned representations. This visualization technique is designed for concept-oriented "model comparison" tasks, allowing data scientists to understand qualitative differences in how models interpret input data. We compare user performance with our tool against Tensor Board Embedding Projector for understanding model accuracy and qualitative model differences. With our tool, users were more accurate and learned strategies for the tasks more quickly. Furthermore, users' analytical process in the comparison condition was positively influenced by using our tool beforehand.