Meta-Learning Representations for Continual Learning

For an agent to continually learn, it must effectively build on previous knowledge and quickly adapt to new input while minimizing the risk of forgetting past learning. Current neural networks typically exhibit pronounced tendencies to forget and lack training focused on facilitating future learning endeavors. Targeting the core of this challenge, OML (Online Meta-Learning) is an objective that zeroes in on catastrophic interference—prioritizing learning representations that are designed to be quick on the uptake for subsequent learning while standing strong against forgetting during online updates. This approach strategically hunts for naturally sparse representations conducive to online updating.

• Directly targets catastrophic interference
• Focuses on learning representations that are quick to adapt and resistant to forgetting
• Achieves naturally sparse representations suited for online updating
• Complements current continual learning strategies
• Competes with rehearsal-based methods despite its simplicity

OML shines a light on the potential to reform how continual learning agents operate, emphasizing representational learning as a pivotal aspect of an efficient and forget-resistant learning process. Such meta-learning techniques could be the key to unlocking more robust and adaptive AI systems. Read More