3D Residual interpolation For Spike Camera Demosaicing

To overcome the high dimensionality problem of data, learning feature representations for clustering has been widely studied. in this work, we propose Disentangling Latent Space Clustering (DLS-Clustering), a new clustering framework that directly learns cluster assignments from disentangled latent spacing without additional clustering methods. We enforce the encoder and the generator of GAN to form an encoder-generator pair in addition to the generator-encoder pair. We train the encoder-generator pair using real data, which can implicitly 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. Our experiments show that the proposed method achieves the optimal disentanglement performance and outperforms existing generative model-based clustering methods.