# Catastrophic forgetting: still a problem for DNNs

## TL;DR

This paper evaluates the challenge of catastrophic forgetting in deep neural networks during class-incremental learning, revealing that existing methods fail under realistic evaluation conditions and emphasizing the need for better solutions.

## Contribution

It introduces a new, application-oriented evaluation procedure for catastrophic forgetting and demonstrates that current methods do not reliably prevent forgetting in realistic scenarios.

## Key findings

- Existing evaluation methods are inadequate for real-world scenarios.
- All tested methods fail to prevent catastrophic forgetting in the experiments.
- The study highlights the need for further research in incremental learning for DNNs.

## Abstract

We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrophic forgetting (CF) behavior is measured using a new evaluation procedure that aims at an application-oriented view of incremental learning. In particular, it imposes that model selection must be performed on the initial dataset alone, as well as demanding that retraining control be performed only using the retraining dataset, as initial dataset is usually too large to be kept. Experiments are conducted on class-incremental problems derived from MNIST, using a variety of different DNN models, some of them recently proposed to avoid catastrophic forgetting. When comparing our new evaluation procedure to previous approaches for assessing CF, we find their findings are completely negated, and that none of the tested methods can avoid CF in all experiments. This stresses the importance of a realistic empirical measurement procedure for catastrophic forgetting, and the need for further research in incremental learning for DNNs.