  We propose a visual analytics system to help a user analyze and steer
zero-shot learning models. Zero-shot learning has emerged as a viable scenario
for categorizing data that consists of no labeled examples, and thus a
promising approach to minimize data annotation from humans. However, it is
challenging to understand where zero-shot learning fails, the cause of such
failures, and how a user can modify the model to prevent such failures. Our
visualization system is designed to help users diagnose and understand
mispredictions in such models, so that they may gain insight on the behavior of
a model when applied to data associated with categories not seen during
training. Through usage scenarios, we highlight how our system can help a user
improve performance in zero-shot learning.
