  In this paper, we design novel interactive deep learning methods to improve
semantic interactions in visual analytics applications. The ability of semantic
interaction to infer analysts' precise intents during sensemaking is dependent
on the quality of the underlying data representation. We propose the
$\text{DeepSI}_{\text{finetune}}$ framework that integrates deep learning into
the human-in-the-loop interactive sensemaking pipeline, with two important
properties. First, deep learning extracts meaningful representations from raw
data, which improves semantic interaction inference. Second, semantic
interactions are exploited to fine-tune the deep learning representations,
which then further improves semantic interaction inference. This feedback loop
between human interaction and deep learning enables efficient learning of user-
and task-specific representations. To evaluate the advantage of embedding the
deep learning within the semantic interaction loop, we compare
$\text{DeepSI}_{\text{finetune}}$ against a state-of-the-art but more basic use
of deep learning as only a feature extractor pre-processed outside of the
interactive loop. Results of two complementary studies, a human-centered
qualitative case study and an algorithm-centered simulation-based quantitative
experiment, show that $\text{DeepSI}_{\text{finetune}}$ more accurately
captures users' complex mental models with fewer interactions.
