arXiv:2606.24007v1 Announce Type: cross Abstract: Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual adaptation. One such natural mechanism is fast and slow adaptation to balance stability and plasticity. This mechanism has deep roots in neuroscience and biology, but there is no consensus on how to best incorporate it in commonly used optimizers. Here, we show that this can be easily done via the VCL framework, where past posteriors are used as priors in the future. Our key idea is to incorporate
The continuous evolution of AI models and their real-world deployment necessitates robust continual learning mechanisms to maintain performance without catastrophic forgetting.
Improving continual learning is crucial for developing versatile and adaptable AI systems that can learn new information over time without requiring extensive retraining from scratch, thus enhancing efficiency and reducing computational costs.
The proposed 'fast and slow adaptation' within the VCL framework offers a more biologically inspired approach to continual learning, potentially leading to more stable and adaptable deep networks.
- · AI developers
- · Continual learning researchers
- · Deep network applications
- · AI systems requiring frequent retraining
- · Brute-force machine learning approaches
More efficient and resource-friendly training and deployment of advanced AI models.
Accelerated development of AI agents capable of sustained, lifelong learning in dynamic environments.
Reduced barriers to entry for deploying complex AI systems in applications with evolving data streams, potentially leading to broader adoption and new use cases.
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Read at arXiv cs.AI