  Context. Variability carries physical patterns and astronomical information
of objects, and stellar light curve variations are essential to understand the
stellar formation and evolution processes. The studies of variations in stellar
photometry have the potential to expand the list of known stars, protostars,
binary stars, and compact objects, which could shed more light on stages of
stellar lifecycles. Aims. The progress in machine-learning techniques and
applications has developed modern algorithms to detect and condense features
from big data, which enables us to classify stellar light curves efficiently
and effectively. Methods. We explore several deep-learning methods on variable
star classifications. The sample of light curves is constructed with delta
Scuti, gamma Doradus, RR Lyrae, eclipsing binaries, and hybrid variables from
Kepler observations. Several algorithms are applied to transform the light
curves into images, continuous wavelet transform (CWT), Gramian angular fields,
and recurrent plots. We also explore the representation ability of these
algorithms. The processed images are fed to several deep-learning methods for
image recognition, including VGG-19, GoogLeNet, Inception-v3, ResNet,
SqueezeNet, and Xception architectures. Results. The best transformation method
is CWT, resulting in an average accuracy of 95.6\%. VGG-19 shows the highest
average accuracy of 93.25\% among all architectures, while it shows the highest
accuracy of 97.2\% under the CWT transformation method. The prediction can
reach about 1000 light curves per second by using NVIDIA RTX 3090. Our results
indicate that the combination of big data and deep learning opens a new path to
classify light curves automatically.
