  Convolutional neural networks (CNNs) are widely used in many image
recognition tasks due to their extraordinary performance. However, training a
good CNN model can still be a challenging task. In a training process, a CNN
model typically learns a large number of parameters over time, which usually
results in different performance. Often, it is difficult to explore the
relationships between the learned parameters and the model performance due to a
large number of parameters and different random initializations. In this paper,
we present a visual analytics approach to compare two different snapshots of a
trained CNN model taken after different numbers of epochs, so as to provide
some insight into the design or the training of a better CNN model. Our system
compares snapshots by exploring the differences in operation parameters and the
corresponding blob data at different levels. A case study has been conducted to
demonstrate the effectiveness of our system.
