arXiv:2505.10457v3 Announce Type: replace-cross Abstract: Incremental learning is a machine learning paradigm where a model learns from a sequential stream of tasks. This setting poses a key challenge: balancing plasticity (learning new tasks) and stability (preserving past knowledge). Neural Architecture Search (NAS), a branch of AutoML, automates the design of the architecture of Deep Neural Networks and has shown success in static settings. However, existing NAS-based approaches to incremental learning often rely on expanding the model at every task, making them impractical in resource-cons
The increasing complexity and resource demands of large AI models highlight the need for more efficient and adaptative learning paradigms, making incremental learning a critical research area.
Improving incremental learning methods, especially through automated architecture search, directly addresses the practical limitations of deploying and maintaining AI systems in dynamic environments and with constrained resources.
This research introduces a novel, expandable neural architecture search approach designed for incremental learning without requiring full model expansion for every new task, offering a more resource-efficient pathway.
- · AI researchers and developers
- · Edge AI computing
- · Adaptive AI systems
- · Resource-constrained AI applications
- · Traditional static AI model deployment
- · Inefficient NAS methods
More robust and sustainable AI systems capable of continuous learning without prohibitive resource demands will emerge.
This could accelerate the deployment of intelligent agents in real-world, dynamic scenarios where constant retraining is infeasible.
The development of highly adaptive and efficient AI could enable new forms of autonomous systems with prolonged operational lifespans and reduced maintenance overhead.
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Read at arXiv cs.AI