Osiris, the innovative methodology proposed in this paper, offers a comprehensive framework for addressing the challenges of unsupervised continual learning (UCL). The study details:

• Distinct Learning Objectives: It uniquely integrates stability, plasticity, and cross-task consolidation within UCL.
• Separate Embedding Spaces: Utilizes multiple embedding spaces to manage different learning objectives, improving task-specific adaptation and generalization.
• State-of-the-Art Results: Achieves superior performance on various UCL benchmarks, setting new standards for unsupervised learning models.
• Practical Implications: The method’s ability to integrate past and present learning experiences without compromising performance makes it ideal for real-world applications requiring adaptive learning capabilities.

This paper not only advances the field of machine learning by enhancing the adaptability and efficiency of unsupervised models but also opens up new possibilities for their application in dynamic environments where learning from sequential and evolving data is crucial.