arXiv:2606.08013v1 Announce Type: new Abstract: Catastrophic forgetting, the abrupt loss of previously acquired knowledge upon learning new information, remains the central challenge in Continual Learning. This project investigates whether the order in which a model learns information affects how well it retains knowledge. Specifically, we ask: does learning general categories first (like "animals" vs "vehicles") before learning specific classes (like "dog" vs "cat") reduce forgetting compared to learning all classes at once? We test three approaches on CIFAR-100: (1) Coarse-to-Fine: train on
The proliferation of complex AI models across diverse applications necessitates more robust and efficient continual learning methods, making catastrophic forgetting a critical current research focus.
Improving continual learning efficiency directly impacts the viability of deploying AI in dynamic, real-world environments that require continuous adaptation without retraining from scratch.
Better understanding of task granularity's effect on catastrophic forgetting could lead to more stable and adaptable AI systems, enhancing their long-term utility and reducing maintenance costs.
- · AI developers
- · Robotics companies
- · Autonomous systems
- · Continual learning research
- · AI retraining services
- · Models with high forgetting rates
AI models become more efficient at learning new information without losing old knowledge.
This could enable more sophisticated and adaptable AI agents in complex environments.
Long-lived, continuously learning AI systems may reduce the need for periodic updates and human intervention in certain tasks.
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Read at arXiv cs.LG