Unsupervised discriminative feature learning via finding a clustering-friendly embedding space

Highlights：

• 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.

摘要

•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.