  Selecting an optimal set of icons is a crucial step in the pipeline of visual
design to structure and navigate through content. However, designing the icons
sets is usually a difficult task for which expert knowledge is required. In
this work, to ease the process of icon set selection to the users, we propose a
similarity metric which captures the properties of style and visual identity.
We train a Siamese Neural Network with an online dataset of icons organized in
visually coherent collections that are used to adaptively sample training data
and optimize the training process. As the dataset contains noise, we further
collect human-rated information on the perception of icon's similarity which
will be used for evaluating and testing the proposed model. We present several
results and applications based on searches, kernel visualizations and optimized
set proposals that can be helpful for designers and non-expert users while
exploring large collections of icons.
