  Conditional generative models have achieved considerable success in the past
few years, but usually require a lot of labeled data. Recently, ClusterGAN
combines GAN with an encoder to achieve remarkable clustering performance via
unsupervised conditional generation. However, it ignores the real conditional
distribution of data, which leads to generating less diverse samples for each
class and makes the encoder only achieve sub-optimal clustering performance.
Here, we propose a new unsupervised conditional generation framework, Double
Cycle-Consistent Conditional GAN (DC3-GAN), which can generate diverse
class-conditioned samples. We enforce the encoder and the generator of GAN to
form an encoder-generator pair in addition to the generator-encoder pair, which
enables us to avoid the low-diversity generation and the triviality of latent
features. We train the encoder-generator pair using real data, which can
indirectly estimate the real conditional distribution. Meanwhile, this
framework enforces the outputs of the encoder to match the inputs of GAN and
the prior noise distribution, which disentangles latent space into two parts:
one-hot discrete and continuous latent variables. The former can be directly
expressed as clusters and the latter represents remaining unspecified factors.
This work demonstrates that enhancing the diversity of unsupervised conditional
generated samples can improve the clustering performance. Experiments on
different benchmark datasets show that the proposed method outperforms existing
generative model-based clustering methods, and also achieves the optimal
disentanglement performance.
