Representation Memorization for Fast Learning New Knowledge without Forgetting
This addresses the challenge of enabling AI systems to learn quickly and adaptively in dynamic real-world environments, representing an incremental improvement over existing continual learning methods.
The paper tackles the problem of catastrophic forgetting and sample efficiency in fast incremental learning of new classes or data distributions, proposing a memory-based Hebbian parameter adaptation method that outperforms state-of-the-art approaches in tasks like image classification and language modeling.
The ability to quickly learn new knowledge (e.g. new classes or data distributions) is a big step towards human-level intelligence. In this paper, we consider scenarios that require learning new classes or data distributions quickly and incrementally over time, as it often occurs in real-world dynamic environments. We propose "Memory-based Hebbian Parameter Adaptation" (Hebb) to tackle the two major challenges (i.e., catastrophic forgetting and sample efficiency) towards this goal in a unified framework. To mitigate catastrophic forgetting, Hebb augments a regular neural classifier with a continuously updated memory module to store representations of previous data. To improve sample efficiency, we propose a parameter adaptation method based on the well-known Hebbian theory, which directly "wires" the output network's parameters with similar representations retrieved from the memory. We empirically verify the superior performance of Hebb through extensive experiments on a wide range of learning tasks (image classification, language model) and learning scenarios (continual, incremental, online). We demonstrate that Hebb effectively mitigates catastrophic forgetting, and it indeed learns new knowledge better and faster than the current state-of-the-art.