Knowledge Capture and Replay for Continual Learning

TL;DR

This paper introduces flashcards, visual representations that capture neural network knowledge for continual learning, effectively preventing forgetting and outperforming existing replay methods across various tasks and datasets.

Contribution

The work presents flashcards as a novel, task-agnostic knowledge replay method that requires only pre-constructed representations, improving continual learning without additional memory overhead.

Findings

Flashcards effectively prevent catastrophic forgetting in continual learning.

Flashcards outperform generative replay methods.

Flashcards match episodic replay performance with less memory use.

Abstract

Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters. Extraction and utilization of encoded knowledge representations are vital when data is no longer available in the future, especially in a continual learning scenario. In this work, we introduce {\em flashcards}, which are visual representations that {\em capture} the encoded knowledge of a network as a recursive function of predefined random image patterns. In a continual learning scenario, flashcards help to prevent catastrophic forgetting and consolidating knowledge of all the previous tasks. Flashcards need to be constructed only before learning the subsequent task, and hence, independent of the number of tasks trained before. We demonstrate the efficacy of flashcards in capturing learned knowledge representation (as an alternative to the original dataset) and empirically validate on a variety of continual learning tasks: reconstruction, denoising, task-incremental learning, and new-instance learning classification, using several heterogeneous benchmark datasets. Experimental evidence indicates that: (i) flashcards as a replay strategy is { \em task agnostic}, (ii) performs better than generative replay, and (iii) is on par with episodic replay without additional memory overhead.