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We propose a visual analytics system to help a user analyze and steerzero-shot learning models. Zero-shot learning has emerged as a viable scenariofor categorizing data that consists of no labeled examples, and thus apromising approach to minimize data annotation from humans. However, it ischallenging to understand where zero-shot learning fails, the cause of suchfailures, and how a user can modify the model to prevent such failures. Ourvisualization system is designed to help users diagnose and understandmispredictions in such models, so that they may gain insight on the behavior ofa model when applied to data associated with categories not seen duringtraining. Through usage scenarios, we highlight how our system can help a userimprove performance in zero-shot learning.

Visually Analyzing and Steering Zero Shot Learning