GCC: Generative Calibration Clustering
Haifeng Xia Hai Huang Zhengming Ding
Deep clustering as an important branch of unsupervised representation
learning focuses on embedding semantically similar samples into the identical
feature space. This core demand inspires the exploration of contrastive
learning and subspace clustering. However, these solutions always rely on the
basic assumption that there are sufficient and category-balanced samples for
generating valid high-level representation. This hypothesis actually is too
strict to be satisfied for real-world applications. To overcome such a
challenge, the natural strategy is utilizing generative models to augment
considerable instances. How to use these novel samples to effectively fulfill
clustering performance improvement is still difficult and under-explored. In
this paper, we propose a novel Generative Calibration Clustering (GCC) method
to delicately incorporate feature learning and augmentation into clustering
procedure. First, we develop a discriminative feature alignment mechanism to
discover intrinsic relationship across real and generated samples. Second, we
design a self-supervised metric learning to generate more reliable cluster
assignment to boost the conditional diffusion generation. Extensive
experimental results on three benchmarks validate the effectiveness and
advantage of our proposed method over the state-of-the-art methods.