SPICE: Semantic Pseudo-labeling for Image Clustering

17 Mar 2021  ·  Chuang Niu, Hongming Shan, Ge Wang ·

The similarity among samples and the discrepancy between clusters are two crucial aspects of image clustering. However, current deep clustering methods suffer from the inaccurate estimation of either feature similarity or semantic discrepancy. In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy. We design two semantics-aware pseudo-labeling algorithms, prototype pseudo-labeling, and reliable pseudo-labeling, which enable accurate and reliable self-supervision over clustering. Without using any ground-truth label, we optimize the clustering network in three stages: 1) train the feature model through contrastive learning to measure the instance similarity, 2) train the clustering head with the prototype pseudo-labeling algorithm to identify cluster semantics, and 3) jointly train the feature model and clustering head with the reliable pseudo-labeling algorithm to improve the clustering performance. Extensive experimental results demonstrate that SPICE achieves significant improvements (~10%) over existing methods and establishes the new state-of-the-art clustering results on six image benchmark datasets in terms of three popular metrics. Importantly, SPICE significantly reduces the gap between unsupervised and fully-supervised classification; e.g., there is only a 2% (91.8% vs 93.8%) accuracy difference on CIFAR-10. Our code has been made publically available at https://github.com/niuchuangnn/SPICE.

PDF Abstract

Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Clustering CIFAR-10 SPICE* Accuracy 0.918 # 4
NMI 0.850 # 5
Train set Train # 1
ARI 0.836 # 5
Backbone ResNet-18 # 1
Image Clustering CIFAR-100 SPICE* Accuracy 0.584 # 4
NMI 0.583 # 4
Train Set Train # 1
ARI 0.422 # 4
Image Clustering ImageNet-10 SPICE (Full ImageNet pre-train) Accuracy 0.969 # 1
NMI 0.927 # 1
ARI 0.933 # 1
Image Clustering Imagenet-dog-15 SPICE Accuracy 0.675 # 6
NMI 0.627 # 6
ARI 0.526 # 6
Image Clustering STL-10 SPICE* Accuracy 0.929 # 3
NMI 0.860 # 3
Train Split Train # 1
Backbone ResNet-34 # 1
Image Clustering Tiny-ImageNet SPICE Accuracy 0.305 # 1
NMI 0.449 # 3
ARI 0.161 # 1