  This paper presents SPICE, a Semantic Pseudo-labeling framework for Image
ClustEring. Instead of using indirect loss functions required by the recently
proposed methods, SPICE generates pseudo-labels via self-learning and directly
uses the pseudo-label-based classification loss to train a deep clustering
network. The basic idea of SPICE is to synergize the discrepancy among semantic
clusters, the similarity among instance samples, and the semantic consistency
of local samples in an embedding space to optimize the clustering network in a
semantically-driven paradigm. Specifically, a semantic-similarity-based
pseudo-labeling algorithm is first proposed to train a clustering network
through unsupervised representation learning. Given the initial clustering
results, a local semantic consistency principle is used to select a set of
reliably labeled samples, and a semi-pseudo-labeling algorithm is adapted for
performance boosting. Extensive experiments demonstrate that SPICE clearly
outperforms the state-of-the-art methods on six common benchmark datasets
including STL10, Cifar10, Cifar100-20, ImageNet-10, ImageNet-Dog, and
Tiny-ImageNet. On average, our SPICE method improves the current best results
by about 10% in terms of adjusted rand index, normalized mutual information,
and clustering accuracy.
