Representation Memorization for Fast Learning New Knowledge without Forgetting

TL;DR

This paper introduces Hebb, a memory-based method that enhances rapid learning of new knowledge while preventing forgetting, by combining memory modules with Hebbian-inspired parameter adaptation, validated across various tasks and scenarios.

Contribution

The paper proposes Hebb, a novel unified framework integrating memory and Hebbian learning principles to improve incremental learning and mitigate catastrophic forgetting.

Findings

Hebb outperforms existing methods in image classification tasks.

It effectively reduces catastrophic forgetting in continual learning.

Demonstrates faster and better learning of new knowledge across tasks.

Abstract

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.