Exploring Machine Teaching for Object Recognition with the Crowd
2019
Teachable interfaces can enable end-users to personalize machine learning applications by explicitly providing a few training examples. They promise higher robustness in the real world by significantly constraining conditions of the learning task to a specific user and their environment. While facilitating user control, their effectiveness can be hindered by lack of expertise or misconceptions. Through a mobile teachable testbed in Amazon Mechanical Turk, we explore how non-experts conceptualize, experience, and reflect on their engagement with machine teaching in the context of object recognition.
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