  The rapid development of Convolutional Neural Networks (CNNs) in recent years
has triggered significant breakthroughs in many machine learning (ML)
applications. The ability to understand and compare various CNN models
available is thus essential. The conventional approach with visualizing each
model's quantitative features, such as classification accuracy and
computational complexity, is not sufficient for a deeper understanding and
comparison of the behaviors of different models. Moreover, most of the existing
tools for assessing CNN behaviors only support comparison between two models
and lack the flexibility of customizing the analysis tasks according to user
needs. This paper presents a visual analytics system, VAC-CNN (Visual Analytics
for Comparing CNNs), that supports the in-depth inspection of a single CNN
model as well as comparative studies of two or more models. The ability to
compare a larger number of (e.g., tens of) models especially distinguishes our
system from previous ones. With a carefully designed model visualization and
explaining support, VAC-CNN facilitates a highly interactive workflow that
promptly presents both quantitative and qualitative information at each
analysis stage. We demonstrate VAC-CNN's effectiveness for assisting novice ML
practitioners in evaluating and comparing multiple CNN models through two use
cases and one preliminary evaluation study using the image classification tasks
on the ImageNet dataset.
