Cross Sample Similarity for Stable Training of GAN

Deep generative model such as generative adversarial networks (GAN) has shown impressive achievements in computer graphics applications. GAN is trained to learn the distribution of target data and is able to generate new samples similar to the original ...

• This paper proposes an improved deep convolutional embedded clustering algorithm using reliable samples.

The deep clustering algorithm can learn the latent features of the embedded subspace, and further realize the clustering of samples in the feature space. The existing deep clustering algorithms mostly integrate neural networks and ...

Generative adversarial network (GAN) is a powerful generative model. However, it suffers from several problems, such as convergence instability and mode collapse. To overcome these drawbacks, this paper presents a novel architecture of GAN, which ...