Architecture-driven Shift: towards a lightweight selector for capturing the trends of logit shift
arXiv:2605.27469v1 Announce Type: new Abstract: Continual Learning (CL) is a practical paradigm to utilize power of deep pre-trained neural networks, but which pre-trained model has a better ability to balance ``Plasticity-Stability", deserving to be chosen? The logit shift serves as a natural proxy because it represents the logit shift in CL scenarios. However, obtaining the logit shift requires huge computational cost, which hinders large-scale model selection. Existing theoretical analyses fail to offer an efficient alternative because of the assumption of uniform hidden layer widths, which
The continuous evolution of AI models and increased deployment of Continual Learning (CL) systems necessitate more efficient model selection techniques, making research into lightweight selectors for logit shift timely.
This research addresses a critical computational bottleneck in Continual Learning, potentially enabling more scalable and practical deployment of advanced AI models by making model selection more efficient.
The ability to efficiently identify optimal pre-trained models for CL scenarios could accelerate the development and deployment of robust AI systems, reducing costs associated with large-scale model selection.
- · AI researchers and developers
- · Companies deploying Continual Learning
- · Edge AI computing
- · Inefficient model selection methods
- · Computational resource-intensive AI development practices
More efficient Continual Learning model selection becomes possible, reducing development cycles and resource usage.
This efficiency could lead to a broader adoption of CL in various applications, improving AI adaptability in dynamic environments.
Reduced computational barriers might democratize advanced AI development, fostering innovation across smaller teams and specialized applications.
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Read at arXiv cs.LG