Zero-shot Clustering of Embeddings with Self-Supervised Learnt Encoders

We explore whether self-supervised pretrained models can provide a useful representation space for datasets they were not trained on, and whether these representations can be used to group novel unlabelled data into meaningful clusters. To this end, we conduct experiments using image representation encoders pretrained on ImageNet using a variety of self-supervised training techniques. These encoders are deployed on image datasets that were not seen during training, without fine-tuning, and we investigate whether their embeddings can be clustered with conventional clustering algorithms. We find that it is possible to create well-defined clusters using self-supervised feature encoders, especially when using the Agglomerative Clustering method, and that it is possible to do so even for very fine-grained datasets such as NABirds. We also find indications that the Silhouette score is a good proxy of cluster quality when no ground-truth is available.