  We train three convolutional neural networks (CNNs) to classify galaxies with
Galaxy Zoo 2 dataset and extract the activations from the last fully connected
layer or the last average pooling layer of CNNs to study the high-dimensional
abstract feature representations of galaxy images. We apply t-Distributed
Stochastic Neighbour Embedding (t-SNE), a popular dimensionality reduction
technique, to visualize the high-dimensional galaxy feature representations in
two-dimensional scatter plots. From the visualization, we try to understand the
galaxy images data itself and obtain some highly valuable insights. For
instance, the learned galaxy feature representations from networks indicate
that the galaxies belonging to the same class tend to group together, i.e. same
morphological galaxies are clustered; The cluster of completely round smooth
galaxy and the cluster of in-between smooth galaxy (between completely round
and cigar-shaped) are moved closer, compared to other clusters; The cluster of
cigar-shaped smooth galaxy and the cluster of edge-on galaxy are intertwined
surprisingly; A galaxy mislabelled as spiral galaxy in the original dataset
falls in the cluster of completely round smooth galaxy, and manual inspection
also identifies out the outlier as a completely round smooth galaxy. These
findings will facilitate the study of galaxy morphology.
