Best Paper
Interactive machine learning (iML) tools help to make ML accessible to users with limited ML expertise. However, gathering necessary training data and expertise for model-building remains challenging. Transfer learning, a process where learned representations from a model trained on potentially terabytes of data can be transferred to a new, related task, offers the possibility of providing "building blocks" for non-expert users to quickly and effectively apply ML in their work. However, transfer learning largely remains an expert tool due to its high complexity. In this paper, we design a prototype to understand non-expert user behavior in an interactive environment that supports transfer learning. Our findings reveal a series of data- and perception-driven decision-making strategies non-expert users employ, to (in)effectively transfer elements using their domain expertise. Finally, we synthesize design implications which might inform future interactive transfer learning environments.
https://doi.org/10.1145/3411764.3445096
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