The self-supervised contrastive learning paradigm can be combined with other unsupervised or semi-supervised techniques to further enhance the generalizability of the learned concepts. One approach is to integrate clustering algorithms with contrastive learning to encourage the model to discover more meaningful and semantically coherent clusters of concepts. By jointly optimizing for both contrastive similarity and cluster coherence, the model can learn more robust and interpretable concepts that generalize well across domains. Additionally, incorporating self-supervised pretext tasks that focus on different aspects of the data, such as rotation prediction or colorization, can provide complementary signals for concept learning and improve the model's ability to capture diverse features and patterns. By leveraging a combination of self-supervised and unsupervised techniques, the model can achieve a more comprehensive and transferable representation of concepts.