Unsupervised Transfer Learning with Self-Supervised Remedy
This work addresses the problem of generalizing deep networks to novel domains without labels, which is incremental as it builds on existing transfer clustering techniques.
The paper tackles unsupervised transfer learning for image clustering by combining transfer learning with self-supervision to improve discriminative clustering in unlabeled target domains, achieving state-of-the-art results on four datasets and competitive performance on zero-shot learning benchmarks.
Generalising deep networks to novel domains without manual labels is challenging to deep learning. This problem is intrinsically difficult due to unpredictable changing nature of imagery data distributions in novel domains. Pre-learned knowledge does not transfer well without making strong assumptions about the learned and the novel domains. Different methods have been studied to address the underlying problem based on different assumptions, e.g. from domain adaptation to zero-shot and few-shot learning. In this work, we address this problem by transfer clustering that aims to learn a discriminative latent space of the unlabelled target data in a novel domain by knowledge transfer from labelled related domains. Specifically, we want to leverage relative (pairwise) imagery information, which is freely available and intrinsic to a target domain, to model the target domain image distribution characteristics as well as the prior-knowledge learned from related labelled domains to enable more discriminative clustering of unlabelled target data. Our method mitigates nontransferrable prior-knowledge by self-supervision, benefiting from both transfer and self-supervised learning. Extensive experiments on four datasets for image clustering tasks reveal the superiority of our model over the state-of-the-art transfer clustering techniques. We further demonstrate its competitive transferability on four zero-shot learning benchmarks.