Unsupervised discriminative feature learning via finding a clustering-friendly embedding space. (September 2022) - Cao, Wenming; Zhang, Zhongfan; Liu, Cheng; Li, Rui; Jiao, Qianfen; Yu, Zhiwen; Wong, Hau-San
Highlights: We exploit the Siamese Network to find a clustering-friendly embedding space to mine highly-reliable pseudo-supervised information for the application of VAT and Conditional-GAN to synthesize cluster-specific samples in the setting of unsupervised learning. We proposed adopting VAT to synthesize samples with different levels of perturbations that can enhance the robustness of Feature Extractor to noise and improve the lower-dimensional latent coding space discovered by the Feature Extractor. We conducted experiments to verify that the latent space discovered by the Feature Extractor can facilitate the Siamese Network to find a clustering-friendly embedding space and extract pseudo-supervised information for VAT and Conditional-GAN. The training of our EDCN involves the adversarial gaming between three players, which not only boosts performance improvement of the clustering but also preserves the cluster-specific information from the Siamese Network in synthesizing samples. Abstract: In this paper, we propose an enhanced deep clustering network (EDCN), which is composed of a Feature Extractor, a Conditional Generator, a Discriminator and a Siamese Network. Specifically, we will utilize two kinds of generated data based on adversarial training, as well as the original data, to train the Feature Extractor for learning effective latent representations. In addition, we adopt the Siamese network to find an embedding space, where a better affinity similarity matrix is obtained as the key to success of spectral clustering in providing reliable pseudo-labels. Particularly, the obtained pseudo-labels will be used to generate realistic data by the Generator. Finally, the discriminator is used to model the real joint distribution of data and corresponding latent representations for Feature Extractor enhancement. To evaluate our proposed EDCN, we conduct extensive experiments on multiple data sets including MNIST, USPS, FRGC, CIFAR-10, STL-10, and Fashion-MNIST by comparing our method with a number of state-of-the-art deep clustering methods, and experimental results demonstrate its effectiveness and superiority.